NDLI: A weighting scheme based on emerging patterns for weighted support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Hongjian Fan Ramamohanarao, K.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Software Eng., Melbourne Univ., Vic., Australia (Hongjian Fan; Ramamohanarao, K.)
Abstract	Support vector machines (SVMs) are powerful tools for solving classification problems and have been applied to many application fields, such as pattern recognition and data mining, in the past decade. Weighted support vector machines (weighted SVMs) extend SVMs by considering that different input vectors make different contributions to the learning of decision surface. An important issue in training weighted SVMs is how to develop a reliable weighting model to reflect the true noise distribution in the training data, i.e., noise and outliers should have low weights. In this paper, we propose to use emerging patterns (EPs) to construct such a model. EPs are those itemsets whose supports in one class are significantly higher than their supports in the other class. Since EPs of a given class represent the discriminating knowledge unique to their home class, noise and outliers should contain no EPs or EPs of the both contradicting classes, while a representative instance of the class should contain strong EPs of the same class. We calculate numeric scores for each instance based on EPs, and then assign weights to the training data using those scores. An extensive experiment carried out on a large number of benchmark datasets show that our weighting scheme often improves the performance of weighted SVMs over SVMs. We argue that the improvement is due to the ability of our model to approximate the true distribution of data points.
Sponsorship	IEEE Comput. Intelligence Soc
Starting Page	435
Ending Page	440
File Size	1375469
Page Count	6
File Format	PDF
ISBN	0780390172
DOI	10.1109/GRC.2005.1547329
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Support vector machine classification Training data Machine learning Pattern recognition Application software Data mining Kernel Software engineering
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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