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COMBINING CLASSIFIERS FOR CREDIT RISK PREDICTION

Journal of Systems Science and Systems Engineering 2009年第3期18卷 292-311页

作者： bhekisipho twala CSIR Modelling and Digital Sciences Unit P.O. Box 395 Pretoria 0001South Africa

Credit risk prediction models seek to predict quality factors such as whether an individual will default （bad applicant） on a loan or not （good applicant）. This can be treated as a kind of machine learning （ML） problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble （combining） classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.

关键词： Supervised learning statistical pattern recognition ensemble credit risk prediction

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State-Based Offloading Model for Improving Response Rate of IoT Services

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Computers, Materials & Continua 2021年第6期67卷 3721-3735页

作者： K.Sakthidasan bhekisipho twala S.Yuvaraj K.Vijayan S.Praveenkumar Prashant Mani C.Bharatiraja Department of Electronics and Communication Engineering Hindustan Institute of Technology and ScienceChennai603103India Faculty of Engineering Built Environment Durban University of Technology(DUT)UniversityDurban4001South Africa SRM Institute of Science and Technology Kattankulathur603203India SRM Institute of Science and Technology NCR CampusModinagar201204India

The Internet of Things(IoT)is a heterogeneous information sharing and access platform that provides services in a pervasive *** and computation offloading in the IoT helps to improve the response rate and the availability of *** offloading in a service-centric IoT environment mitigates the complexity in response delivery and request *** this paper,the state-based task offloading method(STOM)is introduced with a view to maximize the service response rate and reduce the response time of the varying request *** proposed method is designed using the Markov decision-making model to improve the rate of requests *** defining optimal states and filtering the actions based on the probability of response and request analysis,this method achieves less response *** on the defined states,request processing and resource allocations are performed to reduce the backlogs in handling multiple *** proposed method is verified for the response rate and time for the varying requests and processing servers through an experimental *** the experimental analysis,the proposed method is found to improve response rate and reduce backlogs,response time,and offloading factor by 11.5%,20.19%,20.31%,and 8.85%,respectively.

关键词： Decision-making internet of things markov model task offloading task graph

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