NDLI: An Improvement of Centroid-Based Classification Algorithm for Text Classification

Content Provider	IEEE Xplore Digital Library
Author	Cataltepe, Z. Aygun, E.
Copyright Year	2007
Description	Author affiliation: Istanbul Tech.Univ., Istanbul (Cataltepe, Z.; Aygun, E.)
Abstract	k-nearest neighbor and centroid-based classification algorithms are frequently used in text classification due to their simplicity and performance. While k-nearest neighbor algorithm usually performs well in terms of accuracy, it is slow in recognition phase. Because the distances/similarities between the new data point to be recognized and all the training data need to be computed. On the other hand, centroid-based classification algorithms are very fast, because only as many distance/similarity computations as the number of centroids (i.e. classes) needs to be done. In this paper, we evaluate the performance of centroid-based classification algorithm and compare it to nearest mean and nearest neighbor algorithms on 9 data sets. We propose and evaluate an improvement on centroid based classification algorithm. Proposed algorithm starts from the centroids of each class and increases the weight of misclassified training data points on the centroid computation until the validation error starts increasing. The weight increase is done based on the training confusion matrix entries for misclassified points. Vie proposed algorithm results in smaller test error than centroid-based classification algorithm in 7 out of 9 data sets. It is also better than 10-nearest neighbor algorithm in 8 out of 9 data sets. We also evaluate different similarity metrics together with centroid and nearest neighbor algorithms. We find out that, when Euclidean distance is turned into a similarity measure using division as opposed to exponentiation. Euclidean-based similarity can perform almost as good as cosine similarity.
Starting Page	952
Ending Page	956
File Size	179913
Page Count	5
File Format	PDF
ISBN	9781424408313
DOI	10.1109/ICDEW.2007.4401090
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-17
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Text categorization Training data Clustering algorithms Euclidean distance Frequency Classification algorithms Internet Testing Nearest neighbor searches
Content Type	Text
Resource Type	Article

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