Journal ArticleDOI
A novel hybrid feature selection method considering feature interaction in neighborhood rough set
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TLDR
To solve the problem of dimensional reduction in hybrid data with uncertainty and noise, a novel feature selection method is proposed considering the characteristic of interaction in the neighborhood rough set, called NCMI_IFS, which has higher classification performance and is significantly effective.Abstract:
The interaction between features can provide essential information that affects the performances of learning models. Nevertheless, most feature selection methods do not take interaction into account in feature correlations calculation. In this work, to solve the problem of dimensional reduction in hybrid data with uncertainty and noise, a novel feature selection method is proposed considering the characteristic of interaction in the neighborhood rough set. First of all, the multi-neighborhood radii set for hybrid data is obtained according to the distribution characteristics of features. Then, considering the ubiquity of interactive features, the feature correlations are redefined via employing various neighborhood information uncertainty measures. Furthermore, a new objective evaluation function of the interactive selection of hybrid features is developed, which is called the Max-Relevance min-Redundancy Max-Interaction (MRmRMI). Finally, a novel interaction feature selection algorithm based on neighborhood conditional mutual information (NCMI_IFS) is designed. To evaluate the performance of the proposed algorithm, we compare it with other eight representative feature selection algorithms on twenty public datasets. Experimental results on four different classifiers show that the NCMI_IFS algorithm has higher classification performance and is significantly effective.read more
Citations
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Dynamic interaction feature selection based on fuzzy rough set
TL;DR: In this article, a Dynamic Interaction Feature Selection method based on Fuzzy Rough Set (DIFS_FRS) was proposed to deal with the mixed data with fuzziness and inconsistency.
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A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets
TL;DR: Zhang et al. as discussed by the authors proposed a new rough set model of weighted neighborhood probabilistic rough sets (WNPRSs) and investigated its basic properties, while the dependency degree formula of an attribute relative to an attribute subset is defined based on WNPRSs.
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Interactive and Complementary Feature Selection via Fuzzy Multigranularity Uncertainty Measures
TL;DR: In this article , a novel interactive and complementary feature selection approach based on fuzzy multineighborhood rough set model (ICFS_FmNRS) is proposed, which improves the classification performance of feature subsets while reducing the dimension of feature space.
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ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model
TL;DR: In this paper , a network intrusion detection model (ID-RDRL) based on RFE feature extraction and deep reinforcement learning was proposed, which can select the optimal subset of features, remove approximately 80% of redundant features, and learn the selected features through DRL.
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Incremental feature selection by sample selection and feature-based accelerator
TL;DR: Wang et al. as discussed by the authors proposed a novel incremental feature selection method using sample selection and feature-based accelerator to avoid the redundant calculations, which increase the computing and memory space resources.
References
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Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Feature selection based on mutual information: criteria ofmax-dependency, max-relevance, and min-redundancy
TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).
Journal ArticleDOI
Using mutual information for selecting features in supervised neural net learning
TL;DR: This paper investigates the application of the mutual information criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier.
Journal ArticleDOI
Improved heterogeneous distance functions
D. Randall Wilson,Tony Martinez +1 more
TL;DR: This article proposed three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference metric (IVDM), and the Windowed Value Difference measure (WVDM) to handle applications with nominal attributes, continuous attributes and both.
Journal ArticleDOI
Neighborhood rough set based heterogeneous feature subset selection
TL;DR: A neighborhood rough set model is introduced to deal with the problem of heterogeneous feature subset selection and Experimental results show that the neighborhood model based method is more flexible to deals with heterogeneous data.