NDLI: ModBoost for unbiased classification

Content Provider	IEEE Xplore Digital Library
Author	Shah, K. Gala, S. Patil, N.
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. Technol., Univ. of Mumbai, Mumbai, India (Shah, K.; Gala, S.; Patil, N.)
Abstract	Many real world data mining applications involve learning from large data sets. Boosting, an ensemble-based learning algorithm, has shown to improve the performance of classifiers in many situations. In this paper, we describe a new approach that is a modified version of boosting. In the ModBoost method, the sample training set is produced from the given dataset by sampling with replacement. The model is generated from the sample training set and is evaluated for error on the dataset instead of the training set. Misclassified tuples are given a higher weight and subsequently, in the next iterations the misclassified tuples are given first preferences and resampling is done for the rest of the training set. The total weights of the different classes in the new training set are rebalanced. The ModBoost method is evaluated, in terms of the F-measures, Precision, Recall and overall accuracy against large and moderately imbalanced bank marketing data set using Naïve Bayesian as a base classifier. The results are promising and show that the ModBoost method compares well in comparison with AdaBoost and bagging algorithm. Our results prove empirically that ModBoost is an attractive alternative for improving the classification performance. We also provide suggestions for future research.
Starting Page	1
Ending Page	5
File Size	303970
Page Count	5
File Format	PDF
e-ISBN	9781479946747
DOI	10.1109/ICDMIC.2014.6954252
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-05
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Ensembles of classifiers Classification Boosting Prediction algorithms Data models Classification algorithms Data mining Bagging
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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