Variable population‐sized particle swarm optimization for highly imbalanced dataset classification

Abstract

Real-world datasets used for classification often face many challenges when they are imbalanced in nature which is unavoidable and need to be handled by analysts. Many researchers have proposed methods for handling imbalanced datasets and they mostly concentrated on handling binary classification with only two class labels. Only very few research works have been carried out for treating highly imbalanced datasets and fail to handle multiclass datasets. To address imbalance problem in multiclass datasets, this paper proposes a Variable Population sized Particle Swarm Optimization (VPPSO) which is a modified version of Particle Swarm Optimization (PSO) which works based on clustering. PSO usually has fixed population size and has high computational complexity. In order to reduce this, the population size is varied over generations and the particles are loaded into the population iteratively by retaining the balance nature of solutions. PSO optimizes the selection of training and testing samples from each class label in imbalanced datasets for improved classification results. From the implementation results, it is evident that using VPPSO, highly imbalanced datasets with multiclass attributes are classified more efficiently

Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

“…Boostrap samples D1, D2, …Dn are selected from a data set D provide the base classifiers C1, C2, … Cn [15]. Supposing that an optimal number of votes are assigned to a class for randomly selected labels, then the algorithm extracts training object and classifier sets for bootstrapping after which an integration process based on majority voting takes place [16]. The implementation procedure for the bagging algorithm is illustrated in Fig.…”