DT-SVM and Hybrid Approaches for Missing Data Imputation and Classification: A Comprehensive Survey
Keywords:
Data preprocessing, Decision tree, DT-SVM, Ensemble methods, Hybrid classifier, Medical data analysis, Missing value imputation, Support vector machineAbstract
Missing data represents a pervasive challenge in real-world datasets, particularly within medical research and clinical applications, where its presence can substantially degrade the performance of machine learning classifiers and compromise the validity of analytical conclusions. This comprehensive survey paper systematically examines hybrid approaches that integrate decision trees (DT) and support vector machines (SVM) for missing value imputation and subsequent classification, with particular emphasis on the DT-SVM framework and its algorithmic variants. The study provides a thorough exploration of missing data mechanisms, evaluates traditional and machine learning-based imputation techniques, and delineates the theoretical foundations of decision trees and support vector machines. Through critical analysis of existing hybrid methodologies and comparative evaluation against conventional approaches, this review synthesizes current literature to reveal that DT-based imputation, which leverages enhanced attribute correlations within homogeneous data segments identified through recursive partitioning, consistently outperforms simple imputation methods when combined with SVM classification. The survey further examines recent advancements, including approximated k-nearest neighbor (A-kNN) variants that address computational efficiency concerns while maintaining classification accuracy. Key research gaps are identified, including challenges in high-dimensional settings, handling of missing not at random mechanisms, and integration with deep learning architectures. The findings collectively suggest that integrated frameworks such as DT-SVM represent a promising trajectory for achieving robust classification performance in the presence of missing data, with particular relevance to medical diagnosis applications where data quality issues are prevalent and prediction accuracy is paramount.
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