Classifier Comparisons On Credit Approval Prediction

Home / Artificial Intelligence & ML / Classifier Comparisons On Credit Approval Prediction

Classifier Comparisons On Credit Approval Prediction

The objective of this work is to investigate the performance of different classification algorithms using WEKA tool for credit card approval. A major problem in financial analysis is to build an ultimate model that yields fruitful results on certain given information. Neither a single data mining model fulfills all business requirements nor does a business need depend on a single model. Different models must be evaluated to attain the ultimate model. This kind of difficulty could be resolved with the aid of machine learning which could be used directly to obtain the end result with the aid of several artificial intelligent algorithms which perform the role of classifiers. Classification algorithms always find a rule or set of rules to represent data in classes [1]. Financial institutions require rule(s) for making the decision to classify customers into good or bad credit risks. Based on this decision the credit card can be given to specific customers. The popular and well-accepted algorithm for classification task is the induction of decision trees [1], [11]. Another algorithm which is based on probability theory is Naïve Bayes‟ algorithms. Other two algorithms are Artificial Neural Networks (ANN) and Support Vector Machine (SVM). These four classification algorithms have been investigated for their performance. Substantial research has performed these machine learning algorithms. Machine learning covers such a broad range of processes that it is difficult to define precisely. A dictionary definition includes phrases such as to gain knowledge or understanding of or skill by studying the instruction or experience and modification of a behavioral tendency by experienced zoologists and psychologists study learning in animals and humans [15]. The extraction of important information from a large pile of data and its correlations is often the advantage of using machine learning [10]. New knowledge about tasks is constantly being discovered by humans and vocabulary changes. There is a constant stream of new events in the world and continuing redesign of Artificial Intelligent systems to conform to new knowledge is impractical but machine learning methods might be able to track much of it

Research Paper Link: Download Paper

Related Post

Leave a Reply