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MCBC-SMOTE:A Majority Clustering Model for Classification of Imbalanced Data

Computers, Materials & Continua 2022年第12期73卷 4801-4817页

作者： Jyoti Arora meena tushir Keshav Sharma Lalit Mohan Aman Singh Abdullah Alharbi Wael Alosaimi Department of Information Technology MSITGGSIPUNew Delhi110058India Department of Electrical and Electronic Engineering MSITGGSIPUNew Delhi110058India School of Computer Science and Engineering Lovely Professional University144411PunjabIndia Department of Information Technology College of Computers and Information TechnologyTaif University11099Taif 21944Saudi Arabia

Datasets with the imbalanced class distribution are difficult to handle with the standard classification *** supervised learning,dealing with the problem of class imbalance is still considered to be a challenging research *** machine learning techniques are designed to operate on balanced datasets;therefore,the state of the art,different undersampling,over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets,but highly skewed datasets still pose the problem of generalization and noise generation during *** overcome these problems,this paper proposes amajority clusteringmodel for classification of imbalanced datasets known as MCBC-SMOTE(Majority Clustering for balanced Classification-SMOTE).The model provides a method to convert the problem of binary classification into a multi-class *** the proposed algorithm,the number of clusters for themajority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class *** proposed technique is cost-effective,reduces the problem of noise generation and successfully disables the imbalances present in between and within *** results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics.

关键词： Imbalance class problem classification SMOTE k-means clustering sampling

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An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

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Computers, Materials & Continua 2024年第11期81卷 3125-3145页

作者： Sonia Goel meena tushir Jyoti Arora Tripti Sharma Deepali Gupta Ali Nauman Ghulam Muhammad Electrical and Electronics Engineering(EEE)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Information Technology(IT)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Department of Computer Science and Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11421Saudi Arabia

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally *** paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved *** classification methods are ill-suited for incomplete medical *** enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete ***,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification *** effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and *** encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

关键词： Incomplete data nearest neighbor linear interpolation imputation clustering classification

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