Hierarchical Text Classification and Evaluation

@inproceedings{sun01hierarchical, author = {Sun, Aixin and Lim, Ee-Peng }, booktitle = {ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining}, citeulike-article-id = {113108}, comment = {They introduce an evaluation measure for hierarchical classifiers using the concept of "category similarity". Ideally, a classifier that misclassify into categories similar to the correct category are better then algorithms that misclassify into categories different from the correct one. Category similarity is based either on cosine distance between categories or on the distance between categories. They use a standard binary SVM for each node. In each node they decide whether to classify or not the document in the node. Then they also decide for each children whether to descend the hierarchy or not. }, isbn = {0769511198}, keywords = {hierarchical-classifier, hierarchical-measures, real-dataset, structured-output, svm, top-down}, pages = {521--528}, posted-at = {2005-03-03 13:32:11}, priority = {0}, publisher = {IEEE Computer Society}, title = {Hierarchical Text Classification and Evaluation}, url = {http://portal.acm.org/citation.cfm?id=657884}, year = {2001} }

TY - CONF ID - sun01hierarchical L3 - citeulike-article-id:113108 TI - Hierarchical Text Classification and Evaluation T2 - ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining SP - 521 EP - 528 PB - IEEE Computer Society SN - 0769511198 KW - hierarchical-classifier KW - hierarchical-measures KW - real-dataset KW - structured-output KW - svm KW - top-down N1 - They introduce an evaluation measure for hierarchical classifiers using the concept of "category similarity". Ideally, a classifier that misclassify into categories similar to the correct category are better then algorithms that misclassify into categories different from the correct one. Category similarity is based either on cosine distance between categories or on the distance between categories. They use a standard binary SVM for each node. In each node they decide whether to classify or not the document in the node. Then they also decide for each children whether to descend the hierarchy or not. AU - Sun, Aixin AU - Lim, Ee-Peng PY - 2001/// UR - http://portal.acm.org/citation.cfm?id=657884 ER -