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Recent Advances in Feature Selection
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Special Issue "Recent Advances in Feature Selection"

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Deadline for manuscript submissions: 15 September 2023 | Viewed by 13295

Special Issue Editors

Prof. Dr. Basabi Chakraborty

E-Mail Website
Guest Editor
Faculty of Software and Information Science, Iwate Prefectural University, Iwate 020-0693, Japan
Interests: pattern recognition; machine learning; data mining
Dr. Saptarsi Goswami

E-Mail Website
Guest Editor
Computer Science, Bangabasi Morning College, University of Calcutta, Kolkata, India
Interests: machine learning; deep learning; feature selection

Special Issue Information

Dear Colleagues,

Feature selection is of immense importance for the efficient design and development of all pattern recognition and data mining applications. Research in this area has a long history, and many techniques are already available. Recently, with the development of the internet, social media, sensors and communication technologies, the rapid growth of high dimensional data has enhanced the demand for sophisticated machine learning (ML) tools due to their fast and efficient processing. Feature selection is known to improve the performance of machine learning models by reducing the dimensionality of data and computational cost.

The major advancement of deep learning techniques in the area of ML to date has created the opportunity to develop new methods of data representation and feature selection suitable for dealing with deep neural network (DNN) models. DNN models are known for their capability of implicit feature extraction from raw data (especially image and video data) during the process of recognition, which is helpful in some applications. However, knowledge of the explicit relation between the features of the input data and the output classes is very important in many practical applications, such as in healthcare, bioinformatics, banking and finance, telecommunication and decision support systems. Extraction and selection of interpretable features have a great effect on the scientific basis and the performance of a vast majority of real-world AI applications.

This Special Issue calls for contributions from researchers that target the recent developments in the field of feature selection associated with machine learning, including deep learning models for a wide variety of data from both theoretical and practical perspectives.

The topics of interest include, but are not limited to, the following:

New feature selection algorithms;
Evolutionary search-based techniques for feature selection;
Clustering and graph-based techniques for feature selection;
Feature selection for high dimensional data;
Feature selection for time series data ;
Feature selection for textual data;
Feature selection for DNN models;
Deep feature selection;
Ensemble methods for feature selection;
Feature selection applications 

Prof. Dr. Basabi Chakraborty
Dr. Saptarsi Goswami
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • feature selection
  • feature subset selection
  • ensemble feature selection
  • dimensionality reduction
  • interpretable feature
  • deep learning

Published Papers (6 papers)

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Article
E2H Distance-Weighted Minimum Reference Set for Numerical and Categorical Mixture Data and a Bayesian Swap Feature Selection Algorithm
by
Yuto Omae
and
Masaya Mori
Mach. Learn. Knowl. Extr. 2023, 5(1), 109-127; https://doi.org/10.3390/make5010007 - 11 Jan 2023
Viewed by 1037
Abstract
Generally, when developing classification models using supervised learning methods (e.g., support vector machine, neural network, and decision tree), feature selection, as a pre-processing step, is essential to reduce calculation costs and improve the generalization scores. In this regard, the minimum reference set (MRS), [...] Read more.
Generally, when developing classification models using supervised learning methods (e.g., support vector machine, neural network, and decision tree), feature selection, as a pre-processing step, is essential to reduce calculation costs and improve the generalization scores. In this regard, the minimum reference set (MRS), which is a feature selection algorithm, can be used. The original MRS considers a feature subset as effective if it leads to the correct classification of all samples by using the 1-nearest neighbor algorithm based on small samples. However, the original MRS is only applicable to numerical features, and the distances between different classes cannot be considered. Therefore, herein, we propose a novel feature subset evaluation algorithm, referred to as the “E2H distance-weighted MRS,” which can be used for a mixture of numerical and categorical features and considers the distances between different classes in the evaluation. Moreover, a Bayesian swap feature selection algorithm, which is used to identify an effective feature subset, is also proposed. The effectiveness of the proposed methods is verified based on experiments conducted using artificially generated data comprising a mixture of numerical and categorical features. Full article
(This article belongs to the Special Issue Recent Advances in Feature Selection)
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Article
FeaSel-Net: A Recursive Feature Selection Callback in Neural Networks
by
Felix Fischer
,
Alexander Birk
,
Peter Somers
,
Karsten Frenner
,
Cristina Tarín
and
Alois Herkommer
Mach. Learn. Knowl. Extr. 2022, 4(4), 968-993; https://doi.org/10.3390/make4040049 - 31 Oct 2022
Viewed by 1103
Abstract
Selecting only the relevant subsets from all gathered data has never been as challenging as it is in these times of big data and sensor fusion. Multiple complementary methods have emerged for the observation of similar phenomena; oftentimes, many of these techniques are [...] Read more.
Selecting only the relevant subsets from all gathered data has never been as challenging as it is in these times of big data and sensor fusion. Multiple complementary methods have emerged for the observation of similar phenomena; oftentimes, many of these techniques are superimposed in order to make the best possible decisions. A pathologist, for example, uses microscopic and spectroscopic techniques to discriminate between healthy and cancerous tissue. Especially in the field of spectroscopy in medicine, an immense number of frequencies are recorded and appropriately sized datasets are rarely acquired due to the time-intensive measurements and the lack of patients. In order to cope with the curse of dimensionality in machine learning, it is necessary to reduce the overhead from irrelevant or redundant features. In this article, we propose a feature selection callback algorithm (FeaSel-Net) that can be embedded in deep neural networks. It recursively prunes the input nodes after the optimizer in the neural network achieves satisfying results. We demonstrate the performance of the feature selection algorithm on different publicly available datasets and compare it to existing feature selection methods. Our algorithm combines the advantages of neural networks’ nonlinear learning ability and the embedding of the feature selection algorithm into the actual classifier optimization. Full article
(This article belongs to the Special Issue Recent Advances in Feature Selection)
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Article
A Novel Framework for Fast Feature Selection Based on Multi-Stage Correlation Measures
by
Ivan-Alejandro Garcia-Ramirez
,
Arturo Calderon-Mora
,
Andres Mendez-Vazquez
,
Susana Ortega-Cisneros
and
Ivan Reyes-Amezcua
Mach. Learn. Knowl. Extr. 2022, 4(1), 131-149; https://doi.org/10.3390/make4010007 - 08 Feb 2022
Viewed by 2214
Abstract
Datasets with thousands of features represent a challenge for many of the existing learning methods because of the well known curse of dimensionality. Not only that, but the presence of irrelevant and redundant features on any dataset can degrade the performance of any [...] Read more.
Datasets with thousands of features represent a challenge for many of the existing learning methods because of the well known curse of dimensionality. Not only that, but the presence of irrelevant and redundant features on any dataset can degrade the performance of any model where training and inference is attempted. In addition, in large datasets, the manual management of features tends to be impractical. Therefore, the increasing interest of developing frameworks for the automatic discovery and removal of useless features through the literature of Machine Learning. This is the reason why, in this paper, we propose a novel framework for selecting relevant features in supervised datasets based on a cascade of methods where speed and precision are in mind. This framework consists of a novel combination of Approximated and Simulate Annealing versions of the Maximal Information Coefficient (MIC) to generalize the simple linear relation between features. This process is performed in a series of steps by applying the MIC algorithms and cutoff strategies to remove irrelevant and redundant features. The framework is also designed to achieve a balance between accuracy and speed. To test the performance of the proposed framework, a series of experiments are conducted on a large battery of datasets from SPECTF Heart to Sonar data. The results show the balance of accuracy and speed that the proposed framework can achieve. Full article
(This article belongs to the Special Issue Recent Advances in Feature Selection)
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Article
A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model
by
Sourav Malakar
,
Saptarsi Goswami
,
Bhaswati Ganguli
,
Amlan Chakrabarti
,
Sugata Sen Roy
,
K. Boopathi
and
A. G. Rangaraj
Mach. Learn. Knowl. Extr. 2021, 3(4), 946-965; https://doi.org/10.3390/make3040047 - 25 Nov 2021
Cited by 1 | Viewed by 2280
Abstract
Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of [...] Read more.
Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India. Full article
(This article belongs to the Special Issue Recent Advances in Feature Selection)
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Article
A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index
by
Rikta Sen
,
Ashis Kumar Mandal
and
Basabi Chakraborty
Mach. Learn. Knowl. Extr. 2021, 3(4), 771-787; https://doi.org/10.3390/make3040038 - 24 Sep 2021
Cited by 2 | Viewed by 2067
Abstract
Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in [...] Read more.
Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments. Full article
(This article belongs to the Special Issue Recent Advances in Feature Selection)
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Article
Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks
by
Adam Pickens
and
Saptarshi Sengupta
Mach. Learn. Knowl. Extr. 2021, 3(3), 695-719; https://doi.org/10.3390/make3030035 - 31 Aug 2021
Cited by 11 | Viewed by 3050
Abstract
Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important [...] Read more.
Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important to verify the effectiveness of the clustering algorithm in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of two popular clustering mechanisms by comparing their performance over publicly available benchmarking data sets that capture a variety of data point distributions as well as the number of attributes, especially from a computational point of view by incorporating techniques that alleviate some of the issues that plague these algorithms. Sensitivity to initialization conditions and stagnation to local minima are explored. Further, an implementation of a feedforward neural network utilizing a fully connected topology in particle swarm optimization is introduced. This serves to be a guided random search technique for the neural network weight optimization. The algorithms utilized here are studied and compared, from which their applications are explored. The study aims to provide a handy reference for practitioners to both learn about and verify benchmarking results on commonly used real-world data sets from both a supervised and unsupervised point of view before application in more tailored, complex problems. Full article
(This article belongs to the Special Issue Recent Advances in Feature Selection)
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