文章基本信息

标题：An Efficient PSO Based Ensemble Classification Model on High Dimensional Datasets
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作者：G. Lalitha Kumari ; N. Naga Malleswara Rao
期刊名称：International Journal on Soft Computing
电子版ISSN：2229-7103
出版年度：2017
卷号：8
期号：3/4
页码：1
DOI：10.5121/ijsc.2017.8401
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：As the size of the biomedical databases are growing day by day, finding an essential features in the diseaseprediction have become more complex due to high dimensionality and sparsity problems. Also, due to theavailability of a large number of micro-array datasets in the biomedical repositories, it is difficult toanalyze, predict and interpret the feature information using the traditional feature selection basedclassification models. Most of the traditional feature selection based classification algorithms havecomputational issues such as dimension reduction, uncertainty and class imbalance on microarraydatasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its highefficiency, the fast processing speed for real-time applications. The main objective of the feature selectionbased ensemble learning models is to classify the high dimensional data with high computational efficiencyand high true positive rate on high dimensional datasets. In this proposed model an optimized Particleswarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarraydatasets. Experimental results proved that the proposed model has high computational efficiencycompared to the traditional feature selection based classification models in terms of accuracy , truepositive rate and error rate are concerned.
关键词：PSO; Neural network; Ensemble classification; High dimension dataset.