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Publication Type : Conference Paper
Publisher : First International Conference on Human Computer Interactions
Source : Proceedings of the First International Conference on Human Computer Interactions, Saveetha University, Chennai, available in IEEE Digital Library
Url : https://ieeexplore.ieee.org/document/6887821
Campus : Chennai
School : School of Engineering
Department : Computer Science and Engineering
Year : 2013
Abstract : Analyzing the hidden information from the images are helpful to identify the various causes. In general processing of images includes Pre-processing, segmentation, Feature extraction and Classification. Significance of classifier is essential since results are always based on the classifier. The ultimate aim is to explain how WEKA tool is used for rheumatoid arthritis and investigate the performance of the various classifiers for huge data. In our method we are distinctively give attention to the classification methods like ADTree, Best First Decision tree(BF), Decision stump, J48Pruned tree, J48 Graft Pruned tree, Least Absolute Deviation regression trees (LAD), Logistic Model Tree(LMT), Naïve-Bayes (NB), Random tree, Random forest tree, CART Decision tree. The features like Area, perimeter, circularity, integrated density, Median, Skewness, Raw integrated density, and Roundness and solidity are obtained from the Lymphocytes images and formed the data set. Different classifier is applied for RA facet for Validation. RA facet contains 108 rows and 10 columns. Using classifier to find out the various values like Relative Absolute Error, and Kappa Statistic, Mean Absolute Error, Root Mean Squared Error and Root Relative Squared Error. From those values compare with all the methods ADT classifier is suggested for use in huge data.
Cite this Research Publication : SP. Chokkalingam K. Komathy Aug 2013 ‘Comparison of Different Classifier in WEKA for Rheumatoid Arthritis’, Proceedings of the First International Conference on Human Computer Interactions, Saveetha University, Chennai, available in IEEE Digital Library