A Systematic Methodology on Class Imbalanced Problems involved in the Classification of Real-World Datasets
K. Santhi1 , A Rama Mohan Reddy2

1K Santhi, Research Scholar , Department of Computer Science and Engineering, S V University College of Engineering, Tirupati, India.
2A Rama Mohan Reddy, Department of Computer Science and Engineering, S V University College of Engineering , Tirupati, India. 

Manuscript received on 08 August 2019. | Revised Manuscript received on 16 August 2019. | Manuscript published on 30 September 2019. | PP: 7071-7081 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5756098319/2019©BEIESP | DOI: 10.35940/ijrte.C5756.098319
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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: Current generation real-world data sets processed through machine learning are imbalanced by nature. This imbalanced data enables the researchers with a challenging scenario in the context of perdition for both the machine learning and data mining algorithms. It is observed from the past research studies most of the imbalanced data sets consists of the major classes and minor classes and the major class leads the minor class. Several standards and hybrid prediction algorithms are proposed in various application domains but in most of the real-time data sets analyzed in the studies are imbalanced by nature thereby affecting the accuracy of the prediction. This paper presents a systematic survey of the past research studies to analyze intrinsic data characteristics and techniques utilized for handling class-imbalanced data. In addition, this study reveals the research gaps, trends and patterns in existing studies and discusses briefly on future research directions.
Keywords: Data Intrinsic Problem, Imbalanced Data Sets, Machine Learning, Prediction Algorithms.

Scope of the Article: Classification