HYBRID FLOWER POLLINATION ALGORITHM AND SUPPORT VECTOR MACHINE FOR BREAST CANCER CLASSIFICATION

Abstract

Microarray technology is a system that enable experts to examine gene profile at molecular level for early disease detection. Machine learning algorithms such as classification are used in detection of dieses from data generated by microarray. It increases the potentials of classification and diagnosis of many diseases such as cancer at gene expression level. Though, numerous difficulties may affect the performance of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data preprocessing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper proposed a new technique for feature selection and classification of breast cancer based on Flower Pollination algorithm (FPA) and Support Vector machine (SVM) using microarray data. The result for this research reveals that FPA-SVM is promising by outperforming the state of the earth Particle Swam Optimization algorithm with 80.11% accuracy.