Filter-Based Multi-Objective Feature Selection Using NSGA III and Cuckoo Optimization Algorithm

Abstract

Feature selection aims to confiscate inappropriate features and yet improve classification performance. These aims are conflicting with one another, and a choice must be made in the presence of the trade-off between them. Numerous researches deal with feature selection problem but, they are mostly single-objective based. Nowadays, multi-objective optimisation approaches are becoming the most suitable approaches to deal with feature selection problems. They can easily create a balance between selected features and classification accuracy or error rate. Evolutionary computation techniques have been applied for multi-objective feature selection. Cuckoo optimisation algorithm is among the most popular technique that is exceptional in solving the problems of feature selection. Based on the binary cuckoo optimisation algorithm, two different multi-objective filter-based feature selection frameworks are presented with the idea of nondominated sorting genetic algorithms NSGAIII (BCNSG3) along with NSGAII (BCNSG2). Thus, four multi-objective filter-based feature selection approaches are proposed by employing mutual information along with gain ratio based-entropy as the respective filter evaluation measures in all the proposed frameworks. The results obtained are examined and analysed against the existing methods and single objective scheme on fourteen (14) datasets of varying degree of difficulties. The outcome of the experiments displays that the proposed multi-objective algorithms successfully derive a set of nondominated solutions that used the least feature size and attained the best error rate than using full-length features. In general, BCNSG2 obtained the best results compared to the existing methods and single-objective algorithm, whereas BCNSG3 outdoes all other approaches. INDEX TERMS Cuckoo optimization algorithm, multi-objective feature selection, NSGA III, NSGA II, gain ratio based-entropy, mutual information, machine learning and classification.

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

“…In general, the frameworks of the elitist NSGA-III and NSGA-II algorithms are similar, and the only difference between them is the selection mechanism of individuals that are the offspring [41][42][43][44]. e NSGA-II algorithm selects individuals of the same nondominated level based on the calculated crowding distance, whereas the NSGA-III does so using a reference point-based method [45][46][47].…”

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

“…The uniqueness lies in this algorithm's objective preparation, which takes into account linear as well as nonlinear interdependence among feature sets. Usman et al [45] have proposed two alternative multi-objective filterbased FS architectures built on the boolean cuckoo optimization technique, utilizing the concept of non-dominated sorting GAs, NSGAIII (BCNSG3) and NSGAII (BCNSG2). To this end, four different multi-objective filter-based FS techniques were developed, each using MI and gain ratio based entropy as filter assessment measurements.…”

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“…This approach removes the undesired and redundant features of the processed dataset by considering the nonlinear and linear dependencies. Usman et al (2020) proposed a filter based feature selection algorithm using multi objective binary cuckoo optimization algorithm and NSGA-III. They have proposed four multi-objective filter based feature selection techniques by utilizing entropy based on gain ratio and mutual information.…”

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