An Efficient PSO Based Ensemble Classification Model on High Dimensional Datasets

G. Lalitha Kumari1 and N. Naga Malleswara Rao2

1Research Scholar, Acharya Nagarjuna University, Guntur, AP, India

2Professor, Dept. of IT, RVR & JC College of Engineering, Guntur, AP, India

Abstract

As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high  dimensionality and sparsity problems. Also, due to the availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection based ensemble learning models is to classify the high dimensional data with high computational efficiency and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.

Keywords

PSO, Neural network, Ensemble classification, High dimension dataset

Original Source URL: https://aircconline.com/ijsc/V8N4/8417ijsc01.pdf

http://airccse.org/journal/ijsc/current2017.html