A Study on the Applicability of the Memory-Based Reasoning ClassifierA Study on the Applicability of the Memory-Based Reasoning Classifier
- Other Titles
- A Study on the Applicability of the Memory-Based Reasoning Classifier
- Authors
- Beibei Luo; 진서훈; 최종후
- Issue Date
- 2013
- Publisher
- 한국자료분석학회
- Keywords
- classification; MBR; oversampling; model comparison.
- Citation
- Journal of The Korean Data Analysis Society, v.15, no.1, pp.1 - 9
- Indexed
- KCI
- Journal Title
- Journal of The Korean Data Analysis Society
- Volume
- 15
- Number
- 1
- Start Page
- 1
- End Page
- 9
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/105572
- ISSN
- 1229-2354
- Abstract
- In recent year, many methods suitable for classification problems have been extended to include a range of popular techniques, such as neural networks, logistic regression and decision tree induction. Unlike other data mining techniques that use a training set of preclassified data to create a model and then discard the training set, for MBR (memory- based reasoning), the training set essentially is the model. This study gives a way on the memory-based reasoning, decision tree, logistic regression, neural networks and bagging model comparison methods for home equity lines of credit data using 1:1, 1:2, 1:3 and 1:4 target rate datamarts. Through the reasoning underlying their development, MBR classifier can also be a good choice to make a prediction. The proper k for MBR classifier is selected based on the minimum misclassification rate criterion. Under the proper k, we found that the performance of MBR dominated other classification technique for the data set that we analyzed.
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Collections - Graduate School > Department of Applied Statistics > 1. Journal Articles
- College of Public Policy > Division of Big Data Science > 1. Journal Articles
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