Analysis of Feature Selection and Extraction Algorithm for Loan Data: A Big Data Approach

Attigeri, Girija V and Pai, Manohara M.M. and Pai, Radhika M (2017) Analysis of Feature Selection and Extraction Algorithm for Loan Data: A Big Data Approach. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013, 13/09/2017, Manipal.

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Fraudulent activities in financial institutes can break the economic system of the country. These activities can be identified using clustering and classification algorithms. Effectiveness of these algorithms depend on quality of the input data. Moreover, financial data comes from various sources and forms such as financial statements, stakeholders activities and others. This data from various sources is very vast and unstructured big data. Hence, parallel distributed pre-processing is very significant to improve the quality of the data. Objective of this work is dimensionality reduction considering feature selection and extraction algorithm for large volume of financial data. In this paper an attempt is made to understand the implications of feature extraction and transformation algorithm using Principal Feature Analysis on the financial data. Effect of reduced dimension is studied on various classification algorithms for financial loan data. Parallel and distributed implementation is carried out on IBM Bluemix cloud platform with spark notebook. The results show that reduction of features has significantly improved execution time without compromising the accuracy

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Classification; Financial big data; Feature selection and extraction; Support Vector Machine; Logist
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 09 Dec 2017 10:09
Last Modified: 09 Dec 2017 10:09

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