On the merits of building categorization systems by supervised clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical neural networks for text categorization (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Strategies for minimising errors in hierarchical web categorisation
Proceedings of the eleventh international conference on Information and knowledge management
Exploiting Hierarchy in Text Categorization
Information Retrieval
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
A scalability analysis of classifiers in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A practical web-based approach to generating topic hierarchy for text segments
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Automatically learning document taxonomies for hierarchical classification
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Learning hierarchical multi-category text classification models
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bias Analysis in Text Classification for Highly Skewed Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Topic taxonomy adaptation for group profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
An Empirical Study of Utility Measures for k-Anonymisation
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
Tailoring Taxonomies for Efficient Text Categorization and Expert Finding
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Improving taxonomies for large-scale hierarchical classifiers of web documents
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Hierarchy evolution for improved classification
Proceedings of the 20th ACM international conference on Information and knowledge management
Category hierarchy maintenance: a data-driven approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Hierarchical models have been shown to be effective in content classification. However, we observe through empirical study that the performance of a hierarchical model varies with given taxonomies; even a semantically sound taxonomy has potential to change its structure for better classification. By scrutinizing typical cases, we elucidate why a given semantics-based hierarchy does not work well in content classification, and how it could be improved for accurate hierarchical classification. With these understandings, we propose effective localized solutions that modify the given taxonomy for accurate hierarchical classification. We conduct extensive experiments on both toy and real-world data sets, report improved performance and interesting findings, and provide further analysis of algorithmic issues such as time complexity, robustness, and sensitivity to the number of features.