NDLI: A new framework for an adaptive classifier model

Content Provider	IEEE Xplore Digital Library
Author	Iltae Lee Kianmehr, K. Koochakzadeh, N. Alhajj, R. Rokne, J.
Copyright Year	2009
Description	Author affiliation: Dept of Computer Science, University of Calgary, Alberta, Canada (Iltae Lee; Kianmehr, K.; Koochakzadeh, N.; Alhajj, R.; Rokne, J.)
Abstract	In this paper, a new framework to build an adaptive classifier is introduced. At first, a clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to a set of sample data to form initial set of clusters. The clusters are represented as classes. Using support vector machine (SVM), a classifier model is generated. In real world application, data comes in continuously. Therefore, if the model does not learn from the new data, the model may not perform as well with the new data especially when the model's training data is different from the test data. The new framework proposed in this paper rebuilds the classifier model using selected data from test data set to improve the accuracy of the model.
Starting Page	138
Ending Page	144
File Size	125456
Page Count	7
File Format	PDF
ISBN	9781424441143
DOI	10.1109/IRI.2009.5211540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Testing Classification tree analysis Clustering algorithms Data mining Computer science Partitioning algorithms Databases Application software support vector machine adaptive classifiers classification Clustering DBSCAN
Content Type	Text
Resource Type	Article

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