Analysis of feature selection and extraction algorithm for loan data: A big data approach

Pai M.M. Manohara; Girija Attigeri; Radhika M. Pai

doi:10.1109/ICACCI.2017.8126163

Analysis of feature selection and extraction algorithm for loan data: A big data approach

Pai M.M. Manohara, Girija Attigeri, Radhika M. Pai

Department of Information and Communication Technology, Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2147-2151
Number of pages	5
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8126163
Publication status	Published - 30-11-2017
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country/Territory	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

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10.1109/ICACCI.2017.8126163

Cite this

Manohara, P. M. M., Attigeri, G., & Pai, R. M. (2017). Analysis of feature selection and extraction algorithm for loan data: A big data approach. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 2147-2151). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126163

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abstract = "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.",

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Manohara, PMM , Attigeri, G & Pai, RM 2017, Analysis of feature selection and extraction algorithm for loan data: A big data approach. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 2147-2151, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8126163

Analysis of feature selection and extraction algorithm for loan data : A big data approach. / Manohara, Pai M.M.; Attigeri, Girija ; Pai, Radhika M.

2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 2147-2151.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Analysis of feature selection and extraction algorithm for loan data: A big data approach

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