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영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > JIPS (한국정보처리학회)

JIPS (한국정보처리학회)

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한글제목(Korean Title) Feature Selection Using Submodular Approach for Financial Big Data
영문제목(English Title) Feature Selection Using Submodular Approach for Financial Big Data
저자(Author) Girija Attigeri   Manohara Pai M. M.   Radhika M. Pai  
원문수록처(Citation) VOL 15 NO. 06 PP. 1306 ~ 1325 (2019. 12)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such fmancial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is fmding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using sub modular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix Big Data platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.
키워드(Keyword) Classification   Correlation   Feature Subset Select   Financial Big Data   Logistic Regression   Submodula   Optimization   Support Vector Machine  
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