A Fuzzy Logic Based Approach for Data Classification
In this paper, we have developed a new algorithm to handle the classification of data by using fuzzy rules on real world data set. Our proposed algorithm helps banks to decide whether to grant loan to customers by classifying them into three clusters—acce
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Abstract In this paper, we have developed a new algorithm to handle the classification of data by using fuzzy rules on real world data set. Our proposed algorithm helps banks to decide whether to grant loan to customers by classifying them into three clusters—accepted, rejected and those who have probability to get loan. To handle third cluster, fuzzy logic based approach is appropriate. We have implemented our proposed algorithm on standard bank of England data set. Our algorithm makes prediction for getting loan on basis of various attributes like job status, applicant is the chief loan applicant or not, source of income, weight factor etc. Fuzzy rules generated from the numerical data give output in linguistic terms. We have compared our algorithm with the state of the art algorithms—K-Means, Fuzzy C-means etc. Our algorithm has proved to be more efficient than others in terms of performance. Keywords Fuzzy C-means (FCM) algorithm technique
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Fuzzy logic
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Classification
S. Taneja (✉) ⋅ B. Suri ⋅ S. Gupta ⋅ H. Narwal ⋅ A. Jain ⋅ A. Kathuria Department of Computer Science, BPIT, GGSIPU, New Delhi, India e-mail: [email protected] B. Suri e-mail: [email protected] S. Gupta e-mail: [email protected] H. Narwal e-mail: [email protected] A. Jain e-mail: [email protected] A. Kathuria e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 S.C. Satapathy et al. (eds.), Data Engineering and Intelligent Computing, Advances in Intelligent Systems and Computing 542, DOI 10.1007/978-981-10-3223-3_58
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1 Introduction Data Mining [1] is the process of extracting useful information or knowledge from huge amount of data. This information is used to perform strategic operations and thus helps in effective decision making. Decision making generally needs different and new methodologies in order to make sure that decision made is accurate and valid. There are many problems that arise in data analysis. Sometimes, the real world data is uncertain. Thus, it is necessary to develop new techniques to handle the ever increasing and uncertain data. To handle uncertainty in data, we have used the concept of fuzzy logic with classification technique of data mining. As suggested by Zadeh [2], fuzzy set theory deals with ambiguity, uncertainty and seeks to overcome most of the problems generally found in classical set theory. We have taken a data set of loans applied by various applicants to a bank. According to classical set theory, an applicant will either be granted loan or rejected. But, in the real world, there is a probability that he or she might get the loan. Thus, to handle such ambiguous situation, we have defined the membership functions for granting loan to applicants. Our algorithm can be used to perform fuzzy classification where uncertainty or fuzziness can be resolved using membership functions. We have implemented this algorithm using Canopy (python based tool) and various python libraries like Peach 0.3.1 [3], NumPy [4], Matplotlib 1.5.1 [5], Sc
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