Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

Abstract

The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others. The imbalanced datasets are balanced by applying resampling and various solutions are designed to tackle such datasets that mainly focus on class distribution issues. The imbalanced data is rebalanced using these methods. This paper introduces a technique for balancing data through two stages: first, oversampling methods are utilized in the process of rebalancing such imbalanced dataset using the single-point crossover to generate the new data of minority classes, second, it searches for an optimal subset of the imbalanced and balanced datasets by Jellyfish Search (JS) which is an optimization method. Experiments are performed on 18 real imbalanced datasets, and results are compared with famous oversampling methods and the recently published ACOR (Ant Colony Optimization Resampling) method in terms of different appraisal measurements. Higher performance is recorded by the proposed method and comparability with well-known and recent techniques.

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

“…This AJS is one of the newly proposed meta-heuristic swarm-based optimization algorithms derived by simulating the locomotion and dietary patterns [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] of jellyfish. Jellyfish are the most efficient swimmers of all aquatic animals widely seen in the oceans having umbrella-shaped bells and trailing tentacles.…”

“…The key advantages of the ensemble learning mechanism to design a robust feature selection model by proposing combined feature fusion strategies [ 19 , 20 , 21 ], such as combined feature set (CFS), adaptive weighted feature set (AWFS), model-based optimized weighted feature set (MOWFS), and feature-based optimized weighted feature set (FOWFS), are experimented and validated. In order to reduce the losses and selection of optimized weights of those three pre-trained networks, the advantages of a new meta-heuristic optimizer artificial jellyfish optimizer (AJS) [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] was used and finally, the performance of the proposed feature fusion strategies are likened to other combinations of the models with genetic algorithm (GA) [ 30 ] and particle swarm optimization (PSO) [ 31 ] such as MOWFA-GA, MOWFS-PSO, FOWFS-GA, and FOWPS-PSO, and it was observed that the proposed combination of FOWFS-AJS outperforms the other models used for classification of skin lesion diagnosis.…”

Penerapan Metode Oversampling SMOTE Pada Algoritma Random Forest Untuk Prediksi Kebangkrutan Perusahaan

“…Teknik optimasi pada dataset tidak seimbang telah banyak dikembangkan [11]. Beberapa teknik optimasi diantaranya adalah dengan pendekatan level data [12], pendekatan pada level algoritma dan pendekatan pada keduanya [13]. Dengan pendekatan level data, beberapa teknik digunakan dalam proses optimasi, diantaranya adalah melakukan oversampling pada data [14].…”

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

“…They applied the proposed algorithm to ten datasets and compared it with competing algorithms using various metrics; the hybrid algorithm outperformed. Desuky et al 92 used JSO to classify imbalanced and balanced datasets. They performed experiments on 18 real imbalanced datasets, and the proposed method performed comparably with well-known and recently developed techniques.…”

“…Optimizing CNN hyper-parameters JSO Chou et al 90 Predicting peak friction angle of fiber-reinforced soil (FRS) JSO-WFLSSVR Chou et al 93 Classification of concrete as shallow or deep spalling JSO Hoang et al 94 Clustering renewable energy based microgrid JSO Shubham et al 99 Predicting optimal switching angle in voltage control JSO Siddiqui et al 95 Predicting performance of STEACS RVFL-JSO Almodfer et al 89 Benchmark function optimization and data clustering LA-JSO Barshandeh et al 91 Classifying imbalanced and balanced datasets JSO Desuky et al 92 Integrated interval forecasting for solar radiation MOJS Wang and Gao 97 Classifying human brain functions JSO Zhao 98 Forecasting income of rural residents FOGJSO Lei et al 61…”