Figure - available from: Journal of Intelligent Information Systems
This content is subject to copyright. Terms and conditions apply.
![Critical difference diagrams showing the ranks after applying feature selection over the 38 real datasets. For feature selection methods that require a threshold, the option to keep 10%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10\%$$\end{document} is indicated by ‘-10’, the option to stay with 20%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\%$$\end{document} is indicated by ‘-20’, and the option ‘-log’ refers to use log2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$log_2$$\end{document}](publication/375283627/figure/fig3/AS:11431281240305347@1714701817819/Critical-difference-diagrams-showing-the-ranks-after-applying-feature-selection-over-the.png)
Critical difference diagrams showing the ranks after applying feature selection over the 38 real datasets. For feature selection methods that require a threshold, the option to keep 10%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10\%$$\end{document} is indicated by ‘-10’, the option to stay with 20%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\%$$\end{document} is indicated by ‘-20’, and the option ‘-log’ refers to use log2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$log_2$$\end{document}
Source publication
The growth of Big Data has resulted in an overwhelming increase in the volume of data available, including the number of features. Feature selection, the process of selecting relevant features and discarding irrelevant ones, has been successfully used to reduce the dimensionality of datasets. However, with numerous feature selection approaches in t...
Similar publications
We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a given time occasion. Through this procedure, we achieve a dimensionality reduction by selecting the subset of the most informative responses for clustering individuals and simultaneously choosing the o...
The choice of the factorization rank of a matrix is critical, e.g., in dimensionality reduction, filtering, clustering, deconvolution, etc., because selecting a rank that is too high amounts to adjusting the noise, while selecting a rank that is too low results in the oversimplification of the signal. Numerous methods for selecting the factorizatio...
Melanoma, a widespread and hazardous form of cancer, has prompted researchers to prioritize dermoscopic image‐based algorithms for classifying skin lesions. Recently, there has been a growing trend in using pre‐trained convolutional neural networks for detecting skin lesions. However, the features extracted from these classifiers may include irrele...
Hierarchical multi-label classification problems typically deal with datasets with many attributes and labels, which can negatively impact the classifier performance. The application of dimensionality reduction methods can significantly improve the performance of classifiers. Dimensionality reduction can be performed by feature extraction or featur...
This paper introduces a novel graph-based filter method for automatic feature selection (abbreviated as GB-AFS) for multi-class classification tasks. The method determines the minimum combination of features required to sustain prediction performance while maintaining complementary discriminating abilities between different classes. It does not req...
Citations
... This method is known as feature selection based on Mutual Information Maximization (MIM). MIM is widely used and, in most cases, has similar filteringquality performance to other information-theoretic filtering algorithms such as Joint Mutual Information (JMI) and minimum Redundancy -Maximum Relevance (mRMR) [1], [10], [11]. However, MIM feature selection has the critical advantage of requiring only one pass over the dataset and simpler histograms, and for a dataset of N samples and M features its complexity is O(N M ) as opposed to O(N M 2 ) for JMI and mRMR. ...
Feature selection is the data analysis process that selects a smaller and curated subset of the original dataset by filtering out data (features) which are irrelevant or redundant. The most important features can be ranked and selected based on statistical measures, such as mutual information. Feature selection not only reduces the size of dataset as well as the execution time for training Machine Learning (ML) models, but it can also improve the accuracy of the inference. This paper analyses mutual-information-based feature selection for resource-constrained FPGAs and proposes FINESSD, a novel approach that can be deployed for near-storage acceleration. This paper highlights that the Mutual Information Maximization (MIM) algorithm does not require multiple passes over the data while being a good trade-off between accuracy and FPGA resources, when approximated appropriately. The new FPGA accelerator for MIM generated by FINESSD can fully utilize the NVMe bandwidth of a modern SSD and perform feature selection without requiring full dataset transfers onto the main processor. The evaluation using a Samsung SmartSSD over small, large and out-of-core datasets shows that, compared to the mainstream multiprocessing Python ML libraries and an optimized C library, FINESSD yields up to 35x and 19x speedup respectively while being more than 70x more energy efficient for large, out-of-core datasets.
