A review of performance evaluation measures for hierarchical classifiers
作者： Alex A. Freitas , Ana C. Lorena , André C.P.L.F. Carvalho , Eduardo P. Costa
DOI:
关键词: Machine learning 、 Binary number 、 Statistical classification 、 Pattern recognition 、 Computer programming 、 Classifier (UML) 、 Artificial intelligence 、 Computer science
摘要: Criteria for evaluating the performance of a classifier are an important part in its design. They allow to estimate behavior generated on unseen data and can be also used compare against classifiers by other classification algorithms. There currently several measures binary flat problems. For hierarchical problems, where there multiple classes which hierarchically related, evaluation step is more complex. This paper reviews main metrics proposed literature evaluate models.
kent.ac.uk参考文章(21)
Marina Sokolova, Nathalie Japkowicz, Stan Szpakowicz, Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation Lecture Notes in Computer Science. pp. 1015- 1021 ,(2006) , 10.1007/11941439_114
Aixin Sun, Ee-Peng Lim, Wee-Keong Ng, Hierarchical text classification methods and their specification Springer, Boston, MA. pp. 236- 256 ,(2003) , 10.1007/978-1-4615-0435-1_14
Ke Wang, Senqiang Zhou, Shiang Chen Liew, Building Hierarchical Classifiers Using Class Proximity very large data bases. pp. 363- 374 ,(1999)
Nicholas Holden, Alex A. Freitas, HIERARCHICAL CLASSIFICATION OF G-PROTEIN-COUPLED RECEPTORS WITH A PSO/ACO ALGORITHM IEEE Press. ,(2006)
Yu He, Ke Wang, Senqiang Zhou, Hierarchical Classification of Real Life Documents. siam international conference on data mining. pp. 1- 16 ,(2001)
Richard M. Everson, Jonathan E. Fieldsend, Multi-class ROC analysis from a multi-objective optimisation perspective Pattern Recognition Letters. ,vol. 27, pp. 918- 927 ,(2006) , 10.1016/J.PATREC.2005.10.016
Lijuan Cai, Thomas Hofmann, Hierarchical document categorization with support vector machines conference on information and knowledge management. pp. 78- 87 ,(2004) , 10.1145/1031171.1031186
R. Eisner, B. Poulin, D. Szafron, P. Lu, R. Greiner, Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology computational intelligence in bioinformatics and computational biology. pp. 1- 10 ,(2005) , 10.1109/CIBCB.2005.1594940
Karin Verspoor, Judith Cohn, Susan Mniszewski, Cliff Joslyn, A categorization approach to automated ontological function annotation. Protein Science. ,vol. 15, pp. 1544- 1549 ,(2006) , 10.1110/PS.062184006
Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni, Hierarchical classification Proceedings of the 23rd international conference on Machine learning - ICML '06. pp. 177- 184 ,(2006) , 10.1145/1143844.1143867