Department of Information Technology

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Subject: search.filters.subject.SMO

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    A COMPARATIVE STUDY OF CLASSIFICATION ALGORITHM USING ACCIDENT DATA
    (International Journal of Computer Science & Engineering Technology, 2014-10) Priyanka A; Sathiyakumari K
    Road traffic accidents are the majority and severe issue, it results death and injuries of various levels. The traffic control system is one of the main areas, where critical data regarding the society is noted and kept as secured. Various issues of a traffic system like vehicle accidents, traffic volumes and deliberations are recorded at different levels. In connection to this, the accident severities are launched from road traffic accident database. Road traffic accident databases provide the origin for road traffic accident analysis. In this research work, Coimbatore city road traffic databases is taken to consideration, the city having higher number of vehicles and traffic and the city having higher number of vehicles and traffic and the cost of these loss and accidents has a great impact on the socioeconomic growth of a society. Traditional machine learning algorithms are used for developing a decision support system to handle road traffic accident analysis. The algorithms such as SMO, J48, IBK are implemented in Weka version 3.7.9 the result of these algorithms were compared. In this work, the algorithms were tested on a sample database of more than thousand five hundred items, each with 29 accident attributes. And the final result proves that the SMO algorithm was accurate and provides 94%.
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    FUZZY LOGIC BASED IMPROVED SUPPORT VECTOR MACHINE (FISVM) CLASSIFIERFOR HEART DISEASE CLASSIFICATION
    (ARPN Journal of Engineering and Applied Sciences, 2015-09) S, Nithya; C, Suresh Gnana Dhas
    Classification is the major research topic in data mining. Typically classification represents the data to be categorized based on its features or characteristics. This proposed research work aims in developing fuzzy logic based improved support vector machine classifier. Support vector machine is a type of supervised machine learning technique and once when the dataset is given as input it performs the classification task by itself. The proposed classifier aims in improving the classification accuracy of the support vector machine by making use of fuzzy logic. The proposed classifier has been tested on two different datasets namely PIMA Indian diabetes dataset and Z-AlizadehSani dataset in order to classify the occurrence of heart disease among the patients. Performance metrics sensitivity, specificity and classification accuracy are taken for comparison of the proposed fuzzy logic based improved support vector machine classifier (F-ISVM) with several classification algorithms. Results showed that the proposed F-ISVM classifier gives better classification accuracy than that of support vector machine, naive bayes, neural networks, sequential minimal optimization (SMO) and bagging SMO classifiers.
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    AN IMPROVED SUPPORT VECTOR MACHINE (I-SVM) CLASSIFIER FOR HEART DISEASE CLASSIFICATION
    (International Journal of Applied Engineering Research, 2015) S, Nithya; C, Suresh Gnana Dhas
    Classification is the major research issue in data mining. Usually classification represents the data to be categorized based on its features or characteristics. This research work aims in developing an improved support vector machine classifier. Support vector machine is a type of supervised machine learning technique and once when the dataset is given as input it performs the classification task by itself. The proposed classifier aims in improving the classification accuracy of the support vector machine. The proposed classifier has been tested on two different datasets namely PIMA Indian diabetes dataset and Z-Alizadeh Sani dataset in order to classify the occurrence of heart disease among the patients. Performance metrics sensitivity, specificity and classification accuracy are taken for comparison of the proposed improved support vector machine classifier (I-SVM) with several classification algorithms. Results showed that the proposed classifier gives better classification accuracy than that of support vector machine, naive bayes, neural networks, sequential minimal optimization (SMO) and bagging SMO classifiers.