Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
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
The VLDB Journal — The International Journal on Very Large Data Bases
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Weighted isotonic regression under the L1 norm
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Page-level template detection via isotonic smoothing
Proceedings of the 16th international conference on World Wide Web
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Journal of Machine Learning Research
Automatic Construction of N-ary Tree Based Taxonomies
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Learning to rank from structures in hierarchical text classification
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
A two-dimensional Poisson equation formulation of non-parametric statistical non-linear modeling
Computers & Mathematics with Applications
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Hierarchical topic taxonomies have proliferated on the World Wide Web [5, 18], and exploiting the output space decompositions they induce in automated classification systems is an active area of research. In many domains, classifiers learned on a hierarchy of classes have been shown to outperform those learned on a flat set of classes. In this paper we argue that the hierarchical arrangement of classes leads to intuitive relationships between the corresponding classifiers' output scores, and that enforcing these relationships as a post-processing step after classification can improve its accuracy. We formulate the task of smoothing classifier outputs as a regularized isotonic tree regression problem, and present a dynamic programming based method that solves it optimally. This new problem generalizes the classic isotonic tree regression problem, and both, the new formulation and algorithm, might be of independent interest. In our empirical analysis of two real-world text classification scenarios, we show that our approach to smoothing classifier outputs results in improved classification accuracy.