Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
C4.5: programs for machine learning
C4.5: programs for machine learning
Improving text retrieval for the routing problem using latent semantic indexing
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
Boosting to correct inductive bias in text classification
Proceedings of the eleventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Centroid-Based Document Classification: Analysis and Experimental Results
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
A refinement approach to handling model misfit in text categorization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Effect of term distributions on centroid-based text categorization
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
A tutorial on support vector regression
Statistics and Computing
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
K-means clustering versus validation measures: a data distribution perspective
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Using hypothesis margin to boost centroid text classifier
Proceedings of the 2007 ACM symposium on Applied computing
An improved centroid classifier for text categorization
Expert Systems with Applications: An International Journal
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class-feature-centroid classifier for text categorization
Proceedings of the 18th international conference on World wide web
K-means clustering versus validation measures: a data-distribution perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
COG: local decomposition for rare class analysis
Data Mining and Knowledge Discovery
Full border identification for reduction of training sets
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
An A-Team approach to learning classifiers from distributed data sources
International Journal of Intelligent Information and Database Systems
Adapting centroid classifier for document categorization
Expert Systems with Applications: An International Journal
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Cluster-based instance selection for machine classification
Knowledge and Information Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
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Text classification/categorization (TC) is to assign new unlabeled natural language documents to the predefined thematic categories. Centroid-based classifier (CC) has been widely used for TC because of its simplicity and efficiency. However, it has also been long criticized for its relatively low classification accuracy compared with state-of-the-art classifiers such as support vector machines (SVMs). In this paper, we find that for CC using only border instances rather than all instances to construct centroid vectors can obtain higher generalization accuracy. Along this line, we propose Border-Instance-based Iteratively Adjusted Centroid Classifier (IACC_BI), which relies on the border instances found by some routines, e.g. 1-Nearest-and-1-Furthest-Neighbors strategy, to construct centroid vectors for CC. IACC_BI then iteratively adjusts the initial centroid vectors according to the misclassified training instances. Our extensive experiments on 11 real-world text corpora demonstrate that IACC_BI improves the performance of centroid-based classifiers greatly and obtains classification accuracy competitive to the well-known SVMs, while at significantly lower computational costs.