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Solving Imbalanced Data Problem with a New Approach


Affiliations
1 Aurora’s Technological and Research Institute, India
2 Department of CSE, Aurora’s Technological and Research Institute, India
     

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In today’s real world domains, data is increasing at a rapid rate and growing exponentially. Domains like security, Internet, Banking, marketing, finance and others domains had continuous expansion of data. It is very difficult to understand and analyze this raw data.In order to understand this raw data we need some tools, techniques and methodologies so that we can make decision making process easily. There are many knowledge discovery techniques available but the problem of imbalanced data domains is a great challenge in every academy and industry. This imbalanced data problem deals how various algorithms can be applied on imbalanced data and considering their performance levels.

Keywords

Assessment Metrics, Classification, Imbalanced Data, Synthetic Sampling Methods, Support Vector Machines.
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  • Solving Imbalanced Data Problem with a New Approach

Abstract Views: 226  |  PDF Views: 1

Authors

T. Deepthi
Aurora’s Technological and Research Institute, India
M. A. Jabbar
Department of CSE, Aurora’s Technological and Research Institute, India
A. Chandrasekhar Sharma
Aurora’s Technological and Research Institute, India

Abstract


In today’s real world domains, data is increasing at a rapid rate and growing exponentially. Domains like security, Internet, Banking, marketing, finance and others domains had continuous expansion of data. It is very difficult to understand and analyze this raw data.In order to understand this raw data we need some tools, techniques and methodologies so that we can make decision making process easily. There are many knowledge discovery techniques available but the problem of imbalanced data domains is a great challenge in every academy and industry. This imbalanced data problem deals how various algorithms can be applied on imbalanced data and considering their performance levels.

Keywords


Assessment Metrics, Classification, Imbalanced Data, Synthetic Sampling Methods, Support Vector Machines.