Gene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood
This work addresses cancer detection for medical applications, but it is incremental as it builds on existing optimization and classification techniques.
The paper tackles cancer classification using gene expression data by proposing a method that combines Signal to Noise Ratio for gene selection, Multi-Objective Particle Swarm Optimization for feature selection, and Adaptive K-Nearest Neighborhood for classification, resulting in improved accuracy across five datasets compared to recent approaches.
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.