Classifying news stories using memory based reasoning
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
C4.5: programs for machine learning
C4.5: programs for machine learning
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Cluster-based text categorization: a comparison of category search strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th 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
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical neural networks for text categorization (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Automatic Speech and Speaker Recognition: Advanced Topics
Automatic Speech and Speaker Recognition: Advanced Topics
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
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
A refinement approach to handling model misfit in text categorization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A maximal figure-of-merit learning approach to text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Robustness of regularized linear classification methods in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Linear Machine Decision Trees
SVDPACKC (Version 1.0) User''s Guide
SVDPACKC (Version 1.0) User''s Guide
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Building semantic perceptron net for topic spotting
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
A class-feature-centroid classifier for text categorization
Proceedings of the 18th international conference on World wide web
Discriminative word alignment via alignment matrix modeling
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A subspace decision cluster classifier for text classification
Expert Systems with Applications: An International Journal
Universal attribute characterization of spoken languages for automatic spoken language recognition
Computer Speech and Language
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
Local expert forest of score fusion for video event classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Semantic contextual advertising based on the open directory project
ACM Transactions on the Web (TWEB)
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We propose a maximal figure-of-merit (MFoM)-learning approach for robust classifier design, which directly optimizes performance metrics of interest for different target classifiers. The proposed approach, embedding the decision functions of classifiers and performance metrics into an overall training objective, learns the parameters of classifiers in a decision-feedback manner to effectively take into account both positive and negative training samples, thereby reducing the required size of positive training data. It has three desirable properties: (a) it is a performance metric, oriented learning; (b) the optimized metric is consistent in both training and evaluation sets; and (c) it is more robust and less sensitive to data variation, and can handle insufficient training data scenarios. We evaluate it on a text categorization task using the Reuters-21578 dataset. Training an F1-based binary tree classifier using MFoM, we observed significantly improved performance and enhanced robustness compared to the baseline and SVM, especially for categories with insufficient training samples. The generality for designing other metrics-based classifiers is also demonstrated by comparing precision, recall, and F1-based classifiers. The results clearly show consistency of performance between the training and evaluation stages for each classifier, and MFoM optimizes the chosen metric.