Algorithms

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29 pages, 23360 KB

Open AccessArticle

The New Mushroom–Weed Hybrid Reproduction Optimization Algorithm and Its Application to Tourist Route Planning

by Domagoj Palinic, Rea Aladrovic, Marina Ivasic-Kos and Jonatan Lerga

Algorithms 2026, 19(4), 275; https://doi.org/10.3390/a19040275 - 2 Apr 2026

Viewed by 509

Abstract

Nature-inspired metaheuristic algorithms are commonly applied to complex combinatorial optimization problems where exact methods are computationally impractical. Tourist route optimization is a representative multi-objective problem characterized by realistic constraints such as travel time, cost, opening hours, and transportation modes. Although Mushroom Reproduction Optimization [...] Read more.

Nature-inspired metaheuristic algorithms are commonly applied to complex combinatorial optimization problems where exact methods are computationally impractical. Tourist route optimization is a representative multi-objective problem characterized by realistic constraints such as travel time, cost, opening hours, and transportation modes. Although Mushroom Reproduction Optimization is computationally efficient, it often experiences premature convergence in complex search spaces. This paper proposes a novel hybrid algorithm, Mushroom–Weed Hybrid Reproduction Optimization (MWHRO), which integrates the colony-based local search of the Mushroom Reproduction algorithm with the fitness-proportional reproduction and competitive elimination mechanisms of Invasive Weed Optimization. Hybridization enhances population diversity and global exploration while preserving fast convergence. The proposed algorithm is evaluated based on a realistic tourist route optimization problem using real-world data from Zagreb, Croatia, across multiple transportation modes and objective-weight scenarios. Performance is compared against Ant Colony Optimization, Invasive Weed Optimization, Particle Swarm Optimization, and standard Mushroom Reproduction Optimization under equal evaluation budgets. Experimental results demonstrate that the proposed MWHRO algorithm consistently achieves high-quality solutions with significantly lower execution times, particularly in constrained and multimodal scenarios. Statistical analysis confirms the robustness and practical suitability of the proposed approach for real-world route optimization. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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29 pages, 1306 KB

Open AccessArticle

AGRO: An Adaptive Gold Rush Optimizer with Dynamic Strategy Selection

by Costas Panagiotakis

Algorithms 2026, 19(3), 192; https://doi.org/10.3390/a19030192 - 4 Mar 2026

Viewed by 507

Abstract

In this paper, we propose a metaheuristic optimization algorithm called Adaptive Gold Rush Optimizer (AGRO), a substantial evolution of the original Gold Rush Optimizer (GRO). Unlike the standard GRO, which relies on fixed probabilities in the strategy selection process, AGRO utilizes a novel [...] Read more.

In this paper, we propose a metaheuristic optimization algorithm called Adaptive Gold Rush Optimizer (AGRO), a substantial evolution of the original Gold Rush Optimizer (GRO). Unlike the standard GRO, which relies on fixed probabilities in the strategy selection process, AGRO utilizes a novel adaptive mechanism that prioritizes strategies improving solution quality. This adaptive component, which can be applied to any optimization algorithm with fixed probabilities in the strategy selection, adjusts the probabilities of the three core search strategies of GRO (Migration, Collaboration, and Panning), in real time, rewarding those that successfully improve solution quality. Furthermore, AGRO introduces fundamental modifications to the search equations, eliminating the inherent attraction towards the zero coordinates, while explicitly incorporating objective function values to guide prospectors towards promising regions. Experimental results demonstrate that AGRO is highly competitive against ten state-of-the-art algorithms on the twenty-three classical benchmark functions, the CEC2017, and the CEC2019 datasets, offering robust performance across diverse problem landscapes. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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23 pages, 16195 KB

Open AccessArticle

Integrating ShuffleNetV2 with Multi-Scale Feature Extraction and Coordinate Attention Combined with Knowledge Distillation for Apple Leaf Disease Recognition

by Wei-Chia Lo and Chih-Chin Lai

Algorithms 2026, 19(2), 151; https://doi.org/10.3390/a19020151 - 13 Feb 2026

Viewed by 736

Abstract

Misdiagnosing plant diseases often leads to a range of negative consequences, including the overuse of pesticides and unnecessary food waste. Traditionally, identifying diseases on plant leaves has relied on manual visual inspection, making it a complex and time-consuming task. Since the advent of [...] Read more.

Misdiagnosing plant diseases often leads to a range of negative consequences, including the overuse of pesticides and unnecessary food waste. Traditionally, identifying diseases on plant leaves has relied on manual visual inspection, making it a complex and time-consuming task. Since the advent of convolutional neural networks, however, recognition performance for leaf diseases has improved significantly. Most contemporary studies that apply AI techniques to plant-leaf disease classification focus primarily on boosting accuracy, frequently overlooking the limitations posed by resource-constrained real-world environments. To address these challenges, this thesis employs knowledge distillation to enable small models to approximate the recognition capabilities of larger ones. We enhance a ShuffleNetV2-based model by integrating multi-scale feature extraction and a coordinate-attention mechanism, and we further improve the lightweight student model through knowledge distillation to boost its recognition performance. Experimental results show that the proposed model achieves 93.15% accuracy on the Plant Pathology 2021- FGVC8 dataset, utilizing only 0.36 M parameters and 0.0931 GFLOPs. Compared to the ResNet50 baseline, our architecture slashes parameters by nearly 98% while limiting the accuracy gap to a mere 1.6%. These results confirm the model’s ability to maintain robust performance with minimal computational overhead, providing a practical solution for precision agriculture on resource-limited edge devices. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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24 pages, 7462 KB

Open AccessArticle

Graph-Based Pattern Restoration for Occlusion-Robust Human Pose Estimation in Crowded Scenes

by Mansoor Iqbal, Syed Zarak Shah and Zahid Ullah

Algorithms 2026, 19(2), 142; https://doi.org/10.3390/a19020142 - 10 Feb 2026

Viewed by 866

Abstract

Human pose estimation is a core computer vision task with broad applications, yet its performance degrades significantly in crowded scenes and under heavy occlusion due to missing or unreliable visual evidence. To address this limitation, this work reformulates occluded pose estimation as a [...] Read more.

Human pose estimation is a core computer vision task with broad applications, yet its performance degrades significantly in crowded scenes and under heavy occlusion due to missing or unreliable visual evidence. To address this limitation, this work reformulates occluded pose estimation as a structured pattern restoration problem and proposes a graph-based framework that models the human body as a relational skeletal graph. Starting from noisy or incomplete keypoint detections, the proposed method employs a graph neural network to propagate contextual information from visible joints to occluded ones through iterative message passing. Geometry-aware constraints on bone lengths and joint angles are integrated to enforce anatomical plausibility, while an occlusion-aware prediction mechanism distinguishes visible from missing joints during inference. Experiments on COCO-Keypoints, CrowdPose, and OCHuman demonstrate consistent improvements over strong baselines, particularly under moderate and severe occlusion, confirming the effectiveness of structural reasoning for robust pose estimation in real-world environments. These results confirm that explicit structural reasoning enables more accurate, stable, and reliable human pose estimation in real-world, occlusion-heavy environments. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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24 pages, 9471 KB

Open AccessArticle

Algorithmic Complexity vs. Market Efficiency: Evaluating Wavelet–Transformer Architectures for Cryptocurrency Price Forecasting

by Aldan Jay and Rafael Berlanga

Algorithms 2026, 19(2), 101; https://doi.org/10.3390/a19020101 - 27 Jan 2026

Cited by 1 | Viewed by 778

Abstract

We investigate whether sophisticated deep learning architectures justify their computational cost for short-term cryptocurrency price forecasting. Our study evaluates a 2.1M-parameter (M represents millions (e.g., 2.1M = 2,100,000 parameters), with all RMSE values reported in USD) wavelet-enhanced transformer that decomposes the Fear and [...] Read more.

We investigate whether sophisticated deep learning architectures justify their computational cost for short-term cryptocurrency price forecasting. Our study evaluates a 2.1M-parameter (M represents millions (e.g., 2.1M = 2,100,000 parameters), with all RMSE values reported in USD) wavelet-enhanced transformer that decomposes the Fear and Greed Index (FGI) into multiple timescales before integrating these signals with technical indicators. Using Diebold–Mariano tests with HAC-corrected variance, we find that all models—including our wavelet–transformer, ARIMA, XGBoost, LSTM, and vanilla Transformer—fail to significantly outperform the O(1) naive persistence baseline at the 1-day horizon (DM statistic = +19.13,

p < 0.001

, naive preferred). Our model achieves an RMSE of USD 2005 versus USD 1986 for naive (ratio 1.010), requiring 3909× more inference time (2.43 ms vs. 0.0006 ms) for a statistically worse performance. These results provide strong empirical support for the Efficient Market Hypothesis in cryptocurrency markets: even sophisticated multi-scale architectures combining wavelet decomposition, cross-attention, and auxiliary technical indicators cannot extract profitable short-term signals. Through systematic ablation, we identify positional encoding as the only critical architectural component—its removal causes 30% RMSE degradation. Our findings carry important implications, as follows: (1) short-term crypto forecasting faces fundamental predictability limits, (2) architectural complexity provides negative ROI in efficient markets, and (3) rigorous statistical validation reveals that apparent improvements often represent noise rather than signal. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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15 pages, 1389 KB

Open AccessArticle

Comparative Analysis of Real-Time Detection Models for Intelligent Monitoring of Cattle Condition and Behavior

by Oleg Ivashchuk, Zhanat Kenzhebayeva, Alexey Zhigalov, Moldir Allaniyazova, Gulnara Kaziyeva, Kaiyrbek Makulov, Vyacheslav Fedorov and Olga Ivashchuk

Algorithms 2025, 18(12), 763; https://doi.org/10.3390/a18120763 - 2 Dec 2025

Viewed by 708

Abstract

This study benchmarks nine state-of-the-art object detection models on a specialized cattle dataset to assess accuracy and inference speed for real-time agricultural applications. Using a unified protocol without model-specific augmentations, and evaluating all detectors on identical RTX 4090 hardware, we provide a fair [...] Read more.

This study benchmarks nine state-of-the-art object detection models on a specialized cattle dataset to assess accuracy and inference speed for real-time agricultural applications. Using a unified protocol without model-specific augmentations, and evaluating all detectors on identical RTX 4090 hardware, we provide a fair architectural comparison of two-stage, one-stage, and transformer-based models. D_FINE_L and Co_DETR_R_50 achieved the highest accuracy (AP@[0.50:0.95] = 0.872 and 0.851), while RTMDet and YOLOv11_L were the fastest (15.81 and 19.14 ms/image). All models showed substantial accuracy gains on the domain dataset compared to COCO, while maintaining consistent relative speed rankings. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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19 pages, 10374 KB

Open AccessArticle

Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning

by Yicheng Qiu, Li Niu, Feng Sha, Zhaokun Cheng and Keiji Yanai

Algorithms 2025, 18(12), 736; https://doi.org/10.3390/a18120736 - 24 Nov 2025

Cited by 2 | Viewed by 951

Abstract

Neural network pruning is essential for deploying deep learning models on resource-constrained devices by reducing computational and memory demands. In this paper, we propose a novel pruning framework, Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning, which uses information entropy to assess [...] Read more.

Neural network pruning is essential for deploying deep learning models on resource-constrained devices by reducing computational and memory demands. In this paper, we propose a novel pruning framework, Entropy-Guided Search Space Optimization for Efficient Neural Network Pruning, which uses information entropy to assess the importance of convolutional layers. Specifically, we calculate the layer-wise entropy of pretrained weights, apply outlier detection to remove extreme values, and normalize the entropy values. These normalized values guide the selection of retention ratios, ensuring that layers with higher entropy retain more filters. By refining the subnetwork search space, our approach enhances the efficiency of the search process and improves overall subnetwork performance. The refined search space targets more promising regions, reducing computational overhead and resulting in higher-quality pruned networks. Through iterative optimization, the optimal subnetwork is identified and fine-tuned to produce the final pruned model. Experimental results on benchmark datasets show that our method significantly outperforms existing pruning methods, achieving substantial improvements in both accuracy and computational efficiency. This entropy-guided pruning strategy provides a robust and effective solution for neural network compression, suitable for a wide range of deep learning applications. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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21 pages, 23184 KB

Open AccessArticle

FDC-YOLO: A Blur-Resilient Lightweight Network for Engine Blade Defect Detection

by Xinyue Xu, Fei Li, Lanhui Xiong, Chenyu He, Haijun Peng, Yiwen Zhao and Guoli Song

Algorithms 2025, 18(11), 725; https://doi.org/10.3390/a18110725 - 17 Nov 2025

Cited by 1 | Viewed by 886

Abstract

The synergy between continuum robots and visual inspection technology provides an efficient automated solution for aero-engine blade defect detection. However, flexible end-effector instability and complex internal illumination conditions cause defect image blurring and defect feature loss, leading existing detection methods to fail in [...] Read more.

The synergy between continuum robots and visual inspection technology provides an efficient automated solution for aero-engine blade defect detection. However, flexible end-effector instability and complex internal illumination conditions cause defect image blurring and defect feature loss, leading existing detection methods to fail in simultaneously achieving both high-precision and high-speed requirements. To address this, this study proposes the real-time defect detection algorithm FDC-YOLO, enabling precise and efficient identification of blurred defects. We design the dynamic subtractive attention sampling module (DSAS) to dynamically compensate for information discrepancies during sampling, which reduces critical information loss caused by multi-scale feature fusion. We design a high-frequency information processing module (HFM) to enhance defect feature representation in the frequency domain, which significantly improves the visibility of defect regions while mitigating blur-induced noise interference. Additionally, we design a classification domain detection head (CDH) to focus on domain-invariant features across categories. Finally, FDC-YOLO achieves 7.9% and 3.5% mAP improvements on the aero-engine blade defect dataset and low-resolution NEU-DET dataset, respectively, with only 2.68 M parameters and 7.0G FLOPs. These results validate the algorithm’s generalizability in addressing low-accuracy issues across diverse blur artifacts in defect detection. Furthermore, this algorithm is combined with the tensegrity continuum robot to jointly construct an automatic defect detection system for aircraft engines, providing an efficient and reliable innovative solution to the problem of internal damage detection in engines. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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15 pages, 1171 KB

Open AccessArticle

Person Re-Identification Under Non-Overlapping Cameras Based on Advanced Contextual Embeddings

by Chi-Hung Chuang, Tz-Chian Huang, Chong-Wei Wang, Jung-Hua Lo and Chih-Lung Lin

Algorithms 2025, 18(11), 714; https://doi.org/10.3390/a18110714 - 12 Nov 2025

Viewed by 1009

Abstract

Person Re-identification (ReID), a critical technology in intelligent surveillance, aims to accurately match specific individuals across non-overlapping camera networks. However, factors in real-world scenarios such as variations in illumination, viewpoint, and pose continuously challenge the matching accuracy of existing models. Although Transformer-based models [...] Read more.

Person Re-identification (ReID), a critical technology in intelligent surveillance, aims to accurately match specific individuals across non-overlapping camera networks. However, factors in real-world scenarios such as variations in illumination, viewpoint, and pose continuously challenge the matching accuracy of existing models. Although Transformer-based models like TransReID have demonstrated a strong capability for capturing global context in feature extraction, the features they produce still have room for optimization at the metric matching stage. To address this issue, this study proposes a hybrid framework that combines advanced feature extraction with post-processing optimization. We employed a fixed, pre-trained TransReID model as the feature extractor and introduced a camera-aware Jaccard distance re-ranking algorithm (CA-Jaccard) as a post-processing module. Without retraining the main model, this framework refines the initial distance metric matrix by analyzing the local neighborhood topology among feature vectors and incorporating camera information. Experiments were conducted on two major public datasets, Market-1501 and MSMT17. The results show that our framework significantly improved the overall ranking quality of the model, increasing the mean Average Precision (mAP) on Market-1501 from 88.2% to 93.58% compared to using TransReID alone, achieving a gain of nearly 4% in mAP on MSMT17. This research confirms that advanced post-processing techniques can effectively complement powerful feature extraction models, providing an efficient pathway to enhance the robustness of ReID systems in complex scenarios. Additionally, it is the first-ever to analyze how the modified distance metric improves the ReID task when used specifically with the ViT-based feature extractor TransReID. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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28 pages, 4443 KB

Open AccessArticle

UCINet: A Multi-Task Network for Umbilical Coiling Index Measurement in Obstetric Ultrasound

by Zhuofu Liu, Lichen Niu, Zhixin Di and Meimei Liu

Algorithms 2025, 18(9), 592; https://doi.org/10.3390/a18090592 - 22 Sep 2025

Viewed by 1024

Abstract

The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which [...] Read more.

The umbilical coiling index (UCI), which quantifies the degree of vascular coiling in the umbilical cord, is a crucial indicator for assessing fetal intrauterine development and predicting perinatal outcomes. However, the existing methods for measuring the UCI primarily rely on manual assessment, which suffers from low efficiency and susceptibility to inter-observer variability. In response to the challenges in measuring the umbilical coiling index during obstetric ultrasound, we propose UCINet, a multi-task neural network engineered explicitly for this purpose. UCINet demonstrates enhanced operational efficiency and significantly improved accuracy in detection, catering to the nuanced requirements of obstetric imaging. Firstly, this paper proposes a Frequency–Spatial Domain Downsampling Module (FSDM) to extract features in both the frequency and spatial domains, thereby reducing the loss of umbilical cord features and enhancing their representational capacity. The proposed Multi-Receptive Field Feature Perception Module (MRPM) employs receptive fields of varying sizes across different stages of the feature maps, enhancing the richness of feature representation. This approach allows the model to capture a more diverse set of spatial information, contributing to improved overall performance in feature extraction. A Multi-Scale Feature Aggregation Module (MSAM) comprehensively leverages multi-scale features via a dynamic fusion mechanism, optimizing the integration of disparate feature scales for enhanced performance. In addition, the UCI dataset, which consisted of 2018 annotated ultrasound images, was constructed, each labeled with the number of vascular coils and keypoints at both ends of the umbilical cord. Compared with state-of-the-art methods, UCINet achieves consistent improvements across two tasks. In object detection, UCINet outperforms Deformable DETR-R50 with an improvement of 1.2% points in mAP@50. In keypoint localization, it further exceeds YOLOv11 with a 3.0% gain in mAP@50, highlighting its effectiveness in both detection accuracy and fine-grained keypoint prediction. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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27 pages, 1902 KB

Open AccessArticle

Few-Shot Breast Cancer Diagnosis Using a Siamese Neural Network Framework and Triplet-Based Loss

by Tea Marasović and Vladan Papić

Algorithms 2025, 18(9), 567; https://doi.org/10.3390/a18090567 - 8 Sep 2025

Cited by 2 | Viewed by 1343

Abstract

Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the [...] Read more.

Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the ability to secure timely and precise diagnostic results in breast cancer screening. AI technologies offer powerful tools that allow for the effective diagnosis and survival forecasting, reducing the dependency on human cognitive input. Towards this aim, this research introduces a deep meta-learning framework for swift analysis of mammography images—combining a Siamese network model with a triplet-based loss function—to facilitate automatic screening (recognition) of potentially suspicious breast cancer cases. Three pre-trained deep CNN architectures, namely GoogLeNet, ResNet50, and MobileNetV3, are fine-tuned and scrutinized for their effectiveness in transforming input mammograms to a suitable embedding space. The proposed framework undergoes a comprehensive evaluation through a rigorous series of experiments, utilizing two different, publicly accessible, and widely used datasets of digital X-ray mammograms: INbreast and CBIS-DDSM. The experimental results demonstrate the framework’s strong performance in differentiating between tumorous and normal images, even with a very limited number of training samples, on both datasets. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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21 pages, 3825 KB

Open AccessArticle

Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection

by Wenji Ai, Jiaxuan Zou, Zongchao Liu, Shaodi Wang and Shuai Teng

Algorithms 2025, 18(8), 462; https://doi.org/10.3390/a18080462 - 24 Jul 2025

Cited by 3 | Viewed by 1082

Abstract

Achieving state-of-the-art performance (82.5% IoU, 85.6% F1), this paper proposes an enhanced DeepLabV3+ model for robust underwater crack detection through three integrated innovations: a physics-based light propagation correction model for illumination distortion, multi-scale feature extraction for variable crack dimensions, and curvature flow-guided loss [...] Read more.

Achieving state-of-the-art performance (82.5% IoU, 85.6% F1), this paper proposes an enhanced DeepLabV3+ model for robust underwater crack detection through three integrated innovations: a physics-based light propagation correction model for illumination distortion, multi-scale feature extraction for variable crack dimensions, and curvature flow-guided loss for boundary precision. Our approach significantly outperforms DeepLabV3+, SCTNet, and LarvSeg by 10.6–13.4% IoU, demonstrating particular strength in detecting small cracks (78.1% IoU) under challenging low-light/high-turbidity conditions. The solution provides a practical framework for automated underwater infrastructure inspection. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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Review

Jump to: Research, Other

22 pages, 561 KB

Open AccessReview

A Systematic Review of Anomaly and Fault Detection Using Machine Learning for Industrial Machinery

by Syed Haseeb Haider Zaidi, Alex Shenfield, Hongwei Zhang and Augustine Ikpehai

Algorithms 2026, 19(2), 108; https://doi.org/10.3390/a19020108 - 1 Feb 2026

Cited by 2 | Viewed by 2204

Abstract

Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault [...] Read more.

Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault detection within the broader context of predictive maintenance. Following a hybrid review methodology, relevant studies published between 2010 and 2025 were collected from major databases including IEEE Xplore, ScienceDirect, SpringerLink, Scopus, Web of Science, and arXiv. The review categorizes approaches into supervised, unsupervised, and hybrid paradigms, analyzing their pipelines from data collection and preprocessing to model deployment. Findings highlight the effectiveness of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid frameworks in detecting faults from time series and multimodal sensor data. At the same time, key limitations persist, including data scarcity, class imbalance, limited generalizability across equipment types, and a lack of interpretability in deep models. This review concludes that while ML-based predictive maintenance systems are enabling a transition from reactive to proactive strategies, future progress requires improved hybrid architectures, Explainable AI, and scalable real-time deployment. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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43 pages, 554 KB

Open AccessReview

A Survey of Six Classical Classifiers, Including Algorithms, Methodological Characteristics, Foundational Variants, and Recent Advances

by Ali Hussein Alshammari, Gergely Bencsik and Almashhadani Hasnain Ali

Algorithms 2026, 19(1), 37; https://doi.org/10.3390/a19010037 - 1 Jan 2026

Cited by 1 | Viewed by 2291

Abstract

Classification is a core supervised learning task in data analysis, and six classical classifier families (k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes) remain widely used in practice and underpin many subsequent variants. Although both single-family and [...] Read more.

Classification is a core supervised learning task in data analysis, and six classical classifier families (k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes) remain widely used in practice and underpin many subsequent variants. Although both single-family and multi-classifier surveys exist, there is still a gap for a method-centered study that, within a coherent framework, combines algorithmic representations for training and prediction, methodological characteristics, an explicit methodological comparison of the foundational variants within each family, and method-oriented advances published between 2020 and 2025. The survey is organized around a fixed set of performance-related perspectives, including accuracy, hyperparameter tuning, scalability, class imbalance, behavior in high-dimensional settings, decision-boundary complexity, interpretability, computational efficiency, and multiclass handling. It highlights strengths, weaknesses, and trade-offs across the six families and their variants, helping researchers and practitioners select or extend classification approaches. It also outlines future research directions arising from the limitations across the examined methods. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

Other

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83 pages, 3818 KB

Open AccessEditor’s ChoiceSystematic Review

Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review

by Daniele Pelosi, Diletta Cacciagrano and Marco Piangerelli

Algorithms 2025, 18(7), 443; https://doi.org/10.3390/a18070443 - 18 Jul 2025

Cited by 21 | Viewed by 11780

Abstract

Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two distinct [...] Read more.

Explainability and interpretability have emerged as essential considerations in machine learning, particularly as models become more complex and integral to a wide range of applications. In response to increasing concerns over opaque “black-box” solutions, the literature has seen a shift toward two distinct yet often conflated paradigms: explainable AI (XAI), which refers to post hoc techniques that provide external explanations for model predictions, and interpretable AI, which emphasizes models whose internal mechanisms are understandable by design. Meanwhile, the phenomenon of concept and data drift—where models lose relevance due to evolving conditions—demands renewed attention. High-impact events, such as financial crises or natural disasters, have highlighted the need for robust interpretable or explainable models capable of adapting to changing circumstances. Against this backdrop, our systematic review aims to consolidate current research on explainability and interpretability with a focus on concept and data drift. We gather a comprehensive range of proposed models, available datasets, and other technical aspects. By synthesizing these diverse resources into a clear taxonomy, we intend to provide researchers and practitioners with actionable insights and guidance for model selection, implementation, and ongoing evaluation. Ultimately, this work aspires to serve as a practical roadmap for future studies, fostering further advancements in transparent, adaptable machine learning systems that can meet the evolving needs of real-world applications. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))

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