A multi-objective feature selection and classifier ensemble technique for microarray data analysis

Since last few years, microarray technology has got tremendous application in many biomedical researches. Many intelligent models have been developed with different biological interpretation. This work presents a multiobjective feature selection and classifier ensemble (MOFSCE) technique for microarray data. MOFSCE works in two phases. The first phase is a preprocessing step where bi-objective optimisation technique is used to identify the significant genes through Pareto front. Here seven feature ranking approaches are used to develop 21 bi-objective feature selection (BOFS) models. The performance of BOFS model varies with different datasets. Therefore, grading system is used to identify stable BOFS model. In the second phase a classifier ensemble is build up that receives selected features from the identified BOFS model. Output of the classifiers is presented to a harmony search based functional link artificial neural network (HSFLANN) for decision. Performance of MOFSCE is evaluated using seven publicly available microarray datasets.