A novel measure for evaluating classifiers
暂无分享,去创建一个
Qinghua Hu | Jin-Mao Wei | Shu-Qin Wang | Xiao-Jie Yuan | Qinghua Hu | Jinmao Wei | Xiao-Jie Yuan | Shuqin Wang
[1] Vikas Sindhwani,et al. Information Theoretic Feature Crediting in Multiclass Support Vector Machines , 2001, SDM.
[2] Antonio Politi,et al. Measuring spike train synchrony , 2007, Journal of Neuroscience Methods.
[3] R. J. J. H. van Son,et al. A method to quantify the error distribution in confusion matrices , 1995, EUROSPEECH.
[4] Charles X. Ling,et al. Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.
[5] J A Swets,et al. Measuring the accuracy of diagnostic systems. , 1988, Science.
[6] José Hernández-Orallo,et al. Volume under the ROC Surface for Multi-class Problems , 2003, ECML.
[7] Jonathan E. Fieldsend,et al. Multi-class ROC analysis from a multi-objective optimisation perspective , 2006, Pattern Recognit. Lett..
[8] Robert M. Nishikawa,et al. The hypervolume under the ROC hypersurface of "Near-Guessing" and "Near-Perfect" observers in N-class classification tasks , 2005, IEEE Transactions on Medical Imaging.
[9] J. Victor,et al. Nature and precision of temporal coding in visual cortex: a metric-space analysis. , 1996, Journal of neurophysiology.
[10] Robert P. W. Duin,et al. Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Cinthia Obladen de Almendra Freitas,et al. Confusion Matrix Disagreement for Multiple Classifiers , 2007, CIARP.
[12] Constantin F. Aliferis,et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2004, Bioinform..
[13] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[14] Robert P. W. Duin,et al. A simplified extension of the Area under the ROC to the multiclass domain , 2006 .
[15] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[16] J. Hanley,et al. The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.
[17] Lior Rokach,et al. An Introduction to Decision Trees , 2007 .
[18] David J. Hand,et al. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.
[19] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[20] C. Metz. Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.
[21] Yoshua Bengio,et al. Série Scientifique Scientific Series No Unbiased Estimator of the Variance of K-fold Cross-validation No Unbiased Estimator of the Variance of K-fold Cross-validation , 2022 .
[22] Andrew P. Bradley,et al. The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..
[23] Gavin C. Cawley,et al. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers , 2003, Pattern Recognit..
[24] StatnikovAlexander,et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2005 .
[25] J G Daugman,et al. Information Theory and Coding , 1998 .
[26] Tom Fawcett,et al. Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.
[27] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.