Study of Parametric Performance Evaluation of Machine Learning and Statistical Classifiers
Автор: Yugal kumar, G. Sahoo
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 6 Vol. 5, 2013 года.
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Most of the researchers/ scientists are facing data explosion problem presently. Large amount of data is available in the world i.e. data from science, industry, business, survey and many other areas. The main task is how to prune the data and extract valuable information from these data which can be used for decision making. The answer of this question is data mining. Data Mining is popular topic among researchers. There is lot of work that cannot be explored in the field of data mining till now. A large number of data mining tools/software’s are available which are used for mining the valuable information from the datasets and draw new conclusion based on the mined information. These tools used different type of classifiers to classify the data. Many researchers have used different type of tools with different classifiers to obtained desired results. In this paper three classifiers i.e. Bayes, Neural Network and Tree are used with two datasets to obtain desired results. The performance of these classifiers is analyzed with the help of Mean Absolute Error, Root Mean-Squared Error, Time Taken, Correctly Classified Instance, Incorrectly Classified instance and Kappa Statistic parameter.
Bayes Net, J48, Mean Absolute Error, Naive Bayes, Root Mean-Squared Error
Короткий адрес: https://sciup.org/15011914
IDR: 15011914
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