MACHINE LEARNING APPROACH FOR TAXATION ANALYSIS USING CLASSIFICATION TECHNIQUES.

dc.contributor.authorDeepalakshmi R
dc.contributor.authorRadha N
dc.date.accessioned2020-09-07T09:25:19Z
dc.date.available2020-09-07T09:25:19Z
dc.date.issued2011-01
dc.description.abstractData mining process discovers useful information from the hidden data, which can be used for future prediction. Machine learning provides methods, techniques and tools, which help to learn automatically and to make accurate predictions based on past observations. The data are retrieved from the real time environmental setup. Machine learning techniques can help in the integration of computer-based systems in predicting the dataset and to improve the efficiency of the system. The main purpose of this paper is to provide a comparison of some commonly employed classification algorithms under same conditions. Such comparison helps to provide the accurate result in algorithms. Hence comparing the algorithms for such a classifier is a tedious task, for real time dataset. The classification models were experimented by using 365 datasets with 24 attributes. The predicted values for the classifiers were evaluated and the results were compareden_US
dc.identifier.issn0975 – 8887
dc.identifier.urihttps://www.ijcaonline.org/volume12/number10/pxc3872322.pdf
dc.identifier.urihttps://dspace.psgrkcw.com/handle/123456789/1313
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Applicationsen_US
dc.subjectMachine-learning Techniquesen_US
dc.subjectAudit Selection Strategyen_US
dc.subjectData Miningen_US
dc.subjectopen source toolsen_US
dc.subjectNaive bayesen_US
dc.subjectTax auditen_US
dc.subjectWEKA Classificationen_US
dc.titleMACHINE LEARNING APPROACH FOR TAXATION ANALYSIS USING CLASSIFICATION TECHNIQUES.en_US
dc.typeArticleen_US

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