A Recapitulation of Imbalanced Data
Shaheen Layaq1, B. Manjula2
1Shaheen Layaq, Department of Computer Science, Kakatiya University, Warangal, Telangana, India.
2Dr. B. Manjula, Department of Computer Science, Kakatiya University, Warangal, Telangana, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 452-455 | Volume-9 Issue-3, January 2020. | Retrieval Number: B8120129219/2020©BEIESP | DOI: 10.35940/ijitee.B8120.019320
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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: In today’s authentic universe almost all applications are imbalanced. Data imbalance is growing faster than ever before as many systems are interested in extracting knowledge from lake of data. Imbalance issue occurs because required data is very rare and using that rare data if classification is done we may lead to inaccurate result. In few sensitive imbalance cases like medical and finance if classification is done health and wealth both will get a huge lost. It is observed that big data and imbalance issue are having hand in hand relationship. So, imbalance data is gaining much importance in data science. It is predicted that by the year 2020, about 1.7MB of lake of information will be created every second by each device due to development in science and technology. Almost this lake of information generated will be imbalanced. So, in this paper we will define big data and imbalanced data, how there are related to each other, some of the reasons why imbalance data problems are occurring, various areas where imbalance issues is been effecting, current four machine learning methods for imbalanced data (Data based method, Algorithm based method, Cost sensitive method and ensemble methods), overall performance evaluation of imbalance methods are done using a comparison chart and interpreting achievements of imbalanced data using confusion matrix, Combined evaluation measures (G-means, F-Measure, Balanced Accuracy, Youden Index and Matthews’s correlation coefficient) and Graphical performance evaluation using Receiver operating characteristic (ROC) curve and Area under the curve (AUC) and lastly, considering of result of various imbalance methods.
Keywords: Big data, imbalanced data, machine learning, ensembles.
Scope of the Article: Big Data Analytics