Multiclass Data Imbalance Oversampling Techniques (Mudiot) and Random Selection of Features
1V.Shobana , Research Scholar, Department of Computer Science, Chikkanna Government Arts College, Tirupur, India. Assistant Professor, Department of Computer Science, Dr. N. G. P. Arts and Science College, Coimbatore, India.
2Dr. K.Nandhini, Assistant Professor, Department of Computer Science, Chikkanna Government Arts College, Tirupur, India.
Manuscript received on September 18, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 908-914 | Volume-8 Issue-12, October 2019. | Retrieval Number: J92750881019/2019©BEIESP | DOI: 10.35940/ijitee.L9275.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Class imbalance is a serious issue in classification problem. If a class is unevenly distributed the classification algorithm unable to classify the response variable, which will result in inaccuracy. The technique Multiclass Data Imbalance Oversampling Techniques (MuDIOT) is to find out the factors which have a hidden negative impact on classification. To alleviate the negative impact the technique MuDIOT concentrates on balancing the data and the result minimizes the problems raised due to uneven distribution of classes. The dataset chosen has a multiclass distribution problem and it is handled to produce better results of classification.
Keywords: Imbalanced Data, Data Preprocessing, Big Data, Mu DIOT, SMOTE, RFE, Random Forest.
Scope of the Article: Approximation and Randomized Algorithms