NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing pivotal role in the field bioinformatics biomedical research. High-dimensional gene datasets hold wealth information, but their effective utilization hindered by presence irrelevant dimensions noise. The challenge lies extracting meaningful features from these to enhance accuracy classification while maintaining computational efficiency.
 Feature selection crucial step addressing challenges, as it aims identify retain only most informative characteristics large high-dimensional microarray datasets. In context data, characterized its substantial dimensionality, selecting relevant essential for efficient nearest neighbor search, fundamental component various analytical tasks mining.
 Existing feature methods often face issues related trade-off between search efficiency. This paper introduces novel approach, Nearest Neighbor Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed through selection. NNFSRR method focuses on reducing dimensionality dataset identifying removing redundant features, allowing subsequent searches operate solely dimensions.
 OBJECTIVES: primary goal evaluate method's effectiveness improving dimensionality. utilizes correlation efficiency compared existing methods.
 METHODS: uses Uncertainty remove Reduced are used Experiments conducted using real-world datasets, comparisons made based time accuracy.
 RESULTS: demonstrates improved performance, outperforming basic brute force techniques. Selected sets exhibit strong class associations minimizing correlations, enhancing precision.
 CONCLUSION: conclusion, presents promising approach address challenges posed data. It effectively reduces improves accuracy, enhances search. Our experimental results demonstrate that this outperforms techniques terms making valuable tool applications bioinformatics, mining, pattern recognition, biological information retrieval. holds potential advance our understanding complex processes support more accurate classification.