Gene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood

TL;DR

This paper introduces a novel gene selection and classification method combining SNR, MOPSO, and adaptive KNN to improve cancer diagnosis accuracy using microarray data.

Contribution

It presents a new integrated approach for gene selection and classification that enhances accuracy and reduces feature set size in cancer microarray data analysis.

Findings

Improved classification accuracy across five cancer datasets.

Effective reduction of gene features while maintaining high accuracy.

Outperforms recent methods in cancer classification tasks.

Abstract

Cancer detection is one of the key research topics in the medical field. Accurate detection of different cancer types is valuable in providing better treatment facilities and risk minimization for patients. This paper deals with the classification problem of human cancer diseases by using gene expression data. It is presented a new methodology to analyze microarray datasets and efficiently classify cancer diseases. The new method first employs Signal to Noise Ratio (SNR) to find a list of a small subset of non-redundant genes. Then, after normalization, it is used Multi-Objective Particle Swarm Optimization (MOPSO) for feature selection and employed Adaptive K-Nearest Neighborhood (KNN) for cancer disease classification. This method improves the classification accuracy of cancer classification by reducing the number of features. The proposed methodology is evaluated by classifying cancer diseases in five cancer datasets. The results are compared with the most recent approaches, which increases the classification accuracy in each dataset.