文章基本信息

标题：HYBRID CLASSIFICATION APPROACH HDLMM FOR LEARNING DISABILITY PREDICTION IN SCHOOL GOING CHILDREN USING DATA MINING TECHNIQUE
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作者：MARGARET MARY. T ; HANUMANTHAPPA. M
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：13
页码：2989
出版社：Journal of Theoretical and Applied
摘要：Learning Disability is a disorder of neurological condition which causes deficiency in child�s brain activities such as reading, speaking and many other tasks. According to the World Health Organization (WHO), 15% of the children get affected by the learning disability. Efficient prediction and accurate classification is the crucial task for researchers for early detection of learning disability. In this work, our main aim to develop a model for learning disability prediction and classification with the help of soft computing technique. To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. Proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, pre-processing, feature selection and reduction is carried out by measuring of confidence with adaptive genetic algorithm. Prediction and classification is carried out by using Deep Learner Neural network and Markov Model. Genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. The system is implemented using MatLab 2013b. Result analysis shows that the proposed approach is capable to predict the learning disability effectively.
关键词：Learning Disability; Missing Value; Genetic Algorithm; Markov Model and Deep Learner; Hybrid Classification