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A Classifier-free Ensemble Selection Method based on Data Diversity in Random Subspaces
The Ensemble of Classifiers (EoC) has been shown to be effective in impr...
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Genetic-algorithm-optimized neural networks for gravitational wave classification
Gravitational-wave detection strategies are based on a signal analysis t...
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Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a Cluster-Based Class Representation
Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to p...
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Inverse Classification for Comparison-based Interpretability in Machine Learning
In the context of post-hoc interpretability, this paper addresses the ta...
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Fast classification using sparse decision DAGs
In this paper we propose an algorithm that builds sparse decision DAGs (...
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Average Localised Proximity: a new data descriptor with good default one-class classification performance
One-class classification is a challenging subfield of machine learning i...
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An ensemble of Density based Geometric One-Class Classifier and Genetic Algorithm
One of the most rising issues in recent machine learning research is One-Class Classification which considers data set composed of only one class and outliers. It is more reasonable than traditional Multi-Class Classification in dealing with some problematic data set or special cases. Generally, classification accuracy and interpretability for user are considered as trade-off in OCC methods. Classifier based on Hyper-Rectangle (H-RTGL) is a sort of classifier that can be a remedy for such trade-off and uses H-RTGL formulated by conjunction of geometric rules called interval. This interval can be basis of interpretability since it can be easily understood by user. However, existing H-RTGL based OCC classifiers have limitations that (i) most of them cannot reflect density of target class and (ii) that considering density has primitive interval generation method, and (iii) there exists no systematic procedure for hyperparameter of H-RTGL based OCC classifier, which influences classification performance of classifier. Based on these remarks, we suggest One-Class Hyper-Rectangle Descriptor based on density (1-HRD_d) with more elaborate interval generation method including parametric and nonparametric approaches. In addition, we designed Genetic Algorithm (GA) that consists of chromosome structure and genetic operators for systematic generation of 1-HRD_d by optimization of hyperparameter. Our work is validated through a numerical experiment using actual data set with comparison of existing OCC algorithms along with other H-RTGL based classifiers.
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