Multi-objective Optimisation-Based Feature Selection for Multi-label Classification

Khan, Mohammed Arif; Ekbal, Asif; Mencía, Eneldo Loza; Fürnkranz, Johannes

doi:10.1007/978-3-319-59569-6_5

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Therefore, it is justifiable to use an evolutionary algorithm that gives us an approximate solution in an acceptable time. Several studies have successfully applied feature reduction to multi-label problems using evolutionary algorithms [60][61][62][63], but as of today no such technique has been used to solve the label-specific feature learning problem.…”

Section: Evolutionary Optimizationmentioning

confidence: 99%

ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

2020

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Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. However, it suffers from several handicaps: it needs large storage requirements, it is not efficient predicting and presents a low tolerance to noise. The purpose of this paper is to mitigate these effects by adding a data reduction stage. The technique devised, called Prototype selection and Label-Specific Feature Evolutionary Optimization for LDL (ProLSFEO-LDL), is a novel method to simultaneously address the prototype selection and the label-specific feature selection pre-processing techniques. Both techniques pose a complex optimization problem with a huge search space. Therefore, we have proposed a search method based on evolutionary algorithms that allows us to obtain a solution to both problems in a reasonable time. The effectiveness of the proposed ProLSFEO-LDL method is verified on several real-world LDL datasets, showing significant improvements in comparison with using raw datasets.

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Section: Evolutionary Optimizationmentioning

confidence: 99%

ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

2020

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“…1-NN algorithm as a classifier on NSGA-II algorithm for multi-objective feature selection are used in [20]. A multi-objective NSGA-II feature selection algorithm for multi-label data classification with Label Powerset (LP), Binary Relevance (BR), Classifier Chain (CC) and Calibrated Label Ranking (CLR) is used in [21].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

et al. 2020

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It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification. However, deciding on the best subset of features is an NP-Hard problem and plays a key role in improving the prediction accuracy and the processing time of video datasets. In this study, we propose autoencoders for dimensionality reduction of video data sets and ensemble the features extracted by the multi-objective evolutionary Non-dominated Sorting Genetic Algorithm and the autoencoder. We explore the performance of well-known multi-label classification algorithms for video datasets in terms of prediction accuracy and the number of features used. More specifically, we evaluate Non-dominated Sorting Genetic Algorithm-II, autoencoders, ensemble learning algorithms, Principal Component Analysis, Information Gain, and Correlation Based Feature Selection. Some of these algorithms use feature selection techniques to improve the accuracy of the classification. Experiments are carried out with local feature descriptors extracted from two multi-label datasets, the MIR-Flickr dataset which consists of images and the Wireless Multimedia Sensor dataset that we have generated from our video recordings. Significant improvements in the accuracy performance of the algorithms are observed while the number of features are being reduced.

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“…These irrelevant and redundant features do not only increase the computational burden, but also degrade the classification performance of multi-label learning [23,11]. Multi-label feature selection intends to obtain a compact feature subset by selecting relevant features and eliminating the irrelevant and redundant features [36,13,29,8].…”

Section: Introductionmentioning

confidence: 99%

Multi-label feature selection method based on dynamic weight

et al. 2022

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Multi-objective Optimisation-Based Feature Selection for Multi-label Classification

Cited by 7 publications

References 4 publications

ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

Multi-label feature selection method based on dynamic weight

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