Kannan, Balakrishnan; Julie, David M(MECS, November , 2013)
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Abstract:
Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.
Description:
I.J. Intelligent Systems and Applications, 2013, 12, 34-52
Kannan, Balakrishnan; Julie, David M(November 2, 2010)
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Abstract:
The aim of this study is to show the importance of two classification techniques, viz. decision tree and
clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of
all children enrolled in schools. The problems of children with specific learning disabilities have been a
cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and
popular tools used for classification and prediction in Data mining. Different rules extracted from the
decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of
observations into subsets, called clusters, which are useful in finding the different signs and symptoms
(attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the
decision tree and K-means algorithm is used for creating the clusters. By applying these classification
techniques, LD in any child can be identified
Kannan, Balakrishnan; Rafidha Rahiman, K A; Sherly, K B(IEEE, 2011)
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Abstract:
In our study we use a kernel based classification
technique, Support Vector Machine Regression for predicting the
Melting Point of Drug – like compounds in terms of Topological
Descriptors, Topological Charge Indices, Connectivity Indices
and 2D Auto Correlations. The Machine Learning model was
designed, trained and tested using a dataset of 100 compounds
and it was found that an SVMReg model with RBF Kernel could
predict the Melting Point with a mean absolute error 15.5854 and
Root Mean Squared Error 19.7576