Call For Paper Volume:7 Issue:5 May'2020 |

Efficient Machine Learning Classifiers for Automatic Information Class

Publication Date : 18/04/2015

Author(s) :

Dr. S.Vijayarani , N.Nithya.

Volume/Issue :
Volume 2
Issue 4
(04 - 2015)

Abstract :

As the technology keeps on developing to tremendous heights, the maintenance of a huge data becomes harder and harder in day to day life. These huge data cannot be used until it is in an understandable manner. So the method to handle these huge data is done by using data mining methods. Data Mining is defined as extraction of unseen information from the huge set of data. The data mining tasks are classification, prediction, outlier analysis, clustering, association rules, correlation analysis and time series analysis. One of the important domains in data mining, which handles the text data, is called as Text Mining. Text mining generally refers to the process of extracting interesting information and knowledge from unstructured text data. Text mining seeks to extract useful information from unstructured textual data through the identification and exploration of interesting patterns. This research paper uses the concept of machine learning. It pre-processes the dataset, searches for keyword from the dictionary, trains the machine using the algorithms SVM and naïve bayes algorithms. The proposed method is tested and validated using the Annexure II journal list dataset. From the experimental results we analyze that the SVM algorithm produces better results than the Naïve Bayes. The result of the proposed method is used to improve the efficiency of the real time application in both the government and the private agencies.

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Efficient Machine Learning Classifiers for Automatic Information Class

April 16, 2015