Dimentionality reduction based on binary cooperative particle swarm optimization

Abstract

Even though there are numerous classifiers algorithms that are more complex, k-Nearest Neighbour (k-NN) is regarded as one amongst the most successful approaches to solve real-world issues. The classification process’s effectiveness relies on the training set’s data. However, when k-NN classifier is applied to a real world, various issues could arise; for instance, they are considered to be computationally expensive as the complete training set needs to be stored in the computer for classification of the unseen data. Also, intolerance of k-NN classifier towards irrelevant features can be seen. Conversely, imbalance in the training data could occur wherein considerably larger numbers of data could be seen with some classes versus other classes. Thus, selected training data are employed to improve the effectiveness of k-NN classifier when dealing with large datasets. In this research work, a substitute method is present to enhance data selection by simultaneously clubbing the feature selection as well as instances selection pertaining to k-NN classifier by employing Cooperative Binary Particle Swarm Optimisation (CBPSO). This method can also address the constraint of employing the k-nearest neighbour classifier, particularly when handling high dimensional and imbalance data. A comparison study was performed to demonstrate the performance of our approach by employing 20 real world datasets taken from the UCI Machine Learning Repository. The corresponding table of the classification rate demonstrates the algorithm’s performance. The experimental outcomes exhibit the efficacy of our proposed approach.