A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Blocking objectionable web content by leveraging multiple information sources
ACM SIGKDD Explorations Newsletter
Acclimatizing Taxonomic Semantics for Hierarchical Content Classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic taxonomy adaptation for group profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Specializing for predicting obesity and its co-morbidities
Journal of Biomedical Informatics
Distinctive characteristics of a metric using deviations from Poisson for feature selection
Expert Systems with Applications: An International Journal
Sentence-level event classification in unstructured texts
Information Retrieval
Comparison of metrics for feature selection in imbalanced text classification
Expert Systems with Applications: An International Journal
Group Profiling for Understanding Social Structures
ACM Transactions on Intelligent Systems and Technology (TIST)
International Journal of Business Intelligence and Data Mining
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Feature selection is often applied to high-dimensional data as a preprocessing step in text classification. When dealing with highly skewed data, we observe that typical feature selection metrics like information gain or chi-squared are biased toward selecting features for the minor class, and the metric of bi-normal separation can select features for both minor and major classes. In this work, we investigate how these feature selection metrics impact on the performance of frequently used classifiers such as Decision Trees, Na篓ýve Bayes, and Support Vector Machines via bias analysis for highly skewed data. Three types of biases are metric bias, class bias, and classifier bias. Extensive experiments are designed to understand how these biases can be employed in concert and efficiently to achieve good classificationperformance. We report our findings and present recommended approaches to text classification based on bias analysis and the empirical study.