In distributed data environments, classification tasks are challenged by inconsistencies across independently maintained sources. These environments are inherently characterized by high informational uncertainty. Our framework addresses this challeng...
This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. For this, we introduce methods to optimize M...
At present, many Deep Neural Network (DNN) methods have been widely used for hyperspectral image classification. Promising classification results have been obtained by utilizing such models. However, due to the complexity and depth of the model, incr...
The aim of the present study was to compare the predictive performance of decision trees, artificial neural networks, and logistic regression used for the classification of daily body weight gains in beef calves. A total of 680 pure-breed Simmental a...
The decision tree is one of the most important and representative classification algorithms in the field of machine learning, and it is an important technique for solving data mining classification tasks. In this paper, a decision tree classification...
Classification is an important task in the field of machine learning. Decision tree algorithms are a popular choice for handling classification tasks due to their high accuracy, simple algorithmic process, and good interpretability. Traditional decis...
Deep neural networks (DNNs), the integration of neural networks (NNs) and deep learning (DL), have proven highly efficient in executing numerous complex tasks, such as data and image classification. Because the multilayer in a nonlinearly separable d...
Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that co...
Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrum...
In a standard binary supervised classification task, the existence of both negative and positive samples in the training dataset are required to construct a classification model. However, this condition is not met in certain applications where only o...
Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however...
We introduce the fully automatic design of a numerically optimized decision-tree algorithm and demonstrate its application to sea ice classification from SAR data. In the decision tree, an initial multi-class classification problem is split up into a...
This article examines the methods and application of classification trees and multi-criteria decision-making in the process of holding offshore vessels in port (Port State Control—PSC). This work aims to improve the efficiency and precision of...
There are high concentrations of urban spaces and increasingly complex land use types. Providing an efficient and scientific identification of building types has become a major challenge in urban architectural planning. This study used an optimized g...
Multi-instance learning (MIL) is a critical area in machine learning, particularly for applications where data points are grouped into bags. Traditional methods, however, often face challenges in accurately classifying these bags. This paper presents...
In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree–support vector machine classifier is proposed to solve the problem of low identi...
Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm is widely known as a supervised learning model for multi-class classification problems. However, the classification accuracy of the SDTSVM algorithm depends on the perfect selection of...
Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provid...
13 December 2020
Substances that do not degrade over time have proven to be harmful to the environment and are dangerous to living organisms. Being able to predict the biodegradability of substances without costly experiments is useful. Recently, the quantitative str...
The traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this pap...
Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classific...
The detection, mitigation, and classification of power quality (PQ) disturbances have been issues of interest in the power system field. This paper proposes an approach to detect and classify various types of PQ disturbances based on the Stockwell tr...
In recent years, human activity recognition (HAR) has gained significant interest from researchers in the sports and fitness industries. In this study, the authors have proposed a cascaded method including two classifying stages to classify fitness e...
Campaigns significantly impact overall company performance, making the measurement and prediction of campaign efficiency essential. This study proposes an integrated methodology that combines efficiency measurement with efficiency prediction for airl...
An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artifici...
The research concerns data collected in independent sets—more specifically, in local decision tables. A possible approach to managing these data is to build local classifiers based on each table individually. In the literature, many approaches...
Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-ba...
The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction...
Individual tree species identification is important for urban forest inventory and ecology management. Recent advances in remote sensing technologies facilitate more detailed estimation of individual urban tree characteristics. This study presents an...
Vegetation plays a key role in the environmental function of wetlands. The Ramsar-listed wetlands of the Magela Creek floodplain in Northern Australia are identified as being at risk from weeds, fire and climate change. In addition, the floodplain is...
The decision tree algorithm is widely used in various classification problems due to its ease of implementation and strong interpretability. However, information in the real world often has uncertainty and partial reliability, which poses challenges...
With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisour...
Data normalization is a data preprocessing task and one of the first to be performed during intellectual analysis, particularly in the case of tabular data. The importance of its implementation is determined by the need to reduce the sensitivity of t...
Machine learning classification algorithms, such as decision trees and random forests, are commonly used in many applications. Clients who want to classify their data send them to a server that performs their inference using a trained model. The clie...
Machine learning techniques, particularly decision trees, have been extensively utilized in Network-based Intrusion Detection Systems (NIDSs) due to their transparent, rule-based structures that enable straightforward interpretation. However, this tr...
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable...
This study aims to establish a rigorous and effective model to detect enterprises’ financial statements fraud for the sustainable development of enterprises and financial markets. The research period is 2004–2014 and the sample is companies listed on...
The unprecedented rise of deepfake technologies, leveraging sophisticated AI like Generative Adversarial Networks (GANs) and diffusion-based models, presents both opportunities and challenges in terms of digital media authenticity. In response, this...
To beat competition and obtain valuable information, decision-makers must conduct in-depth machine learning or data mining for data analytics. Traditionally, clustering and classification are two common methods used in machine mining. For clustering,...
Cone penetration tests (CPTs) can provide highly accurate and detailed information and characteristics relevant to the stiffness, strength, and consolidation of tested geomaterials, but they do not directly recover real soil samples. Thus, when CPT r...
We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the...
This paper proposes a methodology for deriving an agreement map between the Spanish Land Parcel Information System (LPIS), also known as SIGPAC, and a classification map obtained from multitemporal Sentinel-1 and Sentinel-2 data. The study area compr...
Hydrologic soil groups play an important role in the determination of surface runoff, which, in turn, is crucial for soil and water conservation efforts. Traditionally, placement of soil into appropriate hydrologic groups is based on the judgement of...
Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this s...
We analyze the theoretical properties of the recently proposed objective function for efficient online construction and training of multiclass classification trees in the settings where the label space is very large. We show the important properties...
In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regr...
The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been wide...
Packet classification is a core function of network devices for providing advanced services, with the key challenge being to optimize classification speed while maintaining low memory usage. So far, many have proposed software-based packet classifica...
The aim of this study is to propose a new feature selection method based on the class-based contribution of Shapley values. For this purpose, a clinical decision support system was developed to assist doctors in their diagnosis of lung diseases from...
Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural land...
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