Hedged Predictions for Traditional Chinese Chronic Gastritis Diagnosis with Confidence Machine

Traditional Chinese chronic gastritis diagnosis focuses on producing an accurate classifier and uncovering the predictive confidence for individual instance. Transductive confidence machine (TCM), which is a novel framework that provides hedged prediction coupled with valid confidence. In the framework of TCM, the efficiency of prediction depends on the nonconformity measure of samples. This paper incorporates random forests (RF) to propose a new TCM algorithm named TCM-RF. Our method benefits from the more precise and robust nonconformity measure. A case study of traditional Chinese chronic gastritis demonstrates that TCM-RF is feasible and effective.

[1]  Ulisses Braga-Neto,et al.  Small-sample error estimation: mythology versus mathematics , 2005, SPIE Optics + Photonics.

[2]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[3]  Vladimir Vovk,et al.  A tutorial on conformal prediction , 2007, J. Mach. Learn. Res..

[4]  Tong-Sheng Chen,et al.  Multi-class diagnosis classification on high dimension data by logistic models , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[5]  G. Shafer,et al.  Algorithmic Learning in a Random World , 2005 .

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Alexander Gammerman,et al.  Prediction algorithms and confidence measures based on algorithmic randomness theory , 2002, Theor. Comput. Sci..

[8]  Laurens van der Maaten,et al.  Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation , 2007, MLDM.

[9]  Zhaohui Wu,et al.  Knowledge discovery in traditional Chinese medicine: State of the art and perspectives , 2006, Artif. Intell. Medicine.

[10]  Li Jian-sheng.  Exploration on Intelligent Model Establishment of TCM Syndrome Differentiation Standard , 2007 .

[11]  Vladimir Vovk,et al.  Comparing the Bayes and Typicalness Frameworks , 2001, ECML.

[12]  Alexander Gammerman,et al.  Transduction with Confidence and Credibility , 1999, IJCAI.

[13]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[14]  Changjun Li,et al.  Combination of Principal Component Analysis and Bayesian Network and its Application on Syndrome Classification for Chronic Gastritis in Traditional Chinese Medicine , 2007, Third International Conference on Natural Computation (ICNC 2007).

[15]  Yanjun Qi,et al.  Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources , 2004, Pacific Symposium on Biocomputing.

[16]  Alexander Gammerman,et al.  Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines , 2005, Int. J. Neural Syst..

[17]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[18]  J Peters,et al.  Predictive ecohydrological modelling using the random forest algorithm. , 2005, Communications in agricultural and applied biological sciences.

[19]  Ida G. Sprinkhuizen-Kuyper,et al.  An Overview of Algorithmic Randomness and its Application to Reliable Instance Classification , 2007 .

[20]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[21]  Per Martin-Löf,et al.  The Definition of Random Sequences , 1966, Inf. Control..

[22]  Alexander Gammerman,et al.  Transductive Confidence Machines for Pattern Recognition , 2002, ECML.

[23]  Alexander Gammerman,et al.  Hedging Predictions in Machine Learning: The Second Computer Journal Lecture , 2006, Comput. J..

[24]  A. Kolmogorov.  Three approaches to the quantitative definition of information , 1968 .

[25]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[26]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[27]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[28]  Xu Wen-li.  Thinking and approaches on treatment of chronic gastritis with integration of traditional Chinese and western medicine , 2001 .

[29]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[30]  Siu Cheung Hui,et al.  Computational methods for Traditional Chinese Medicine: A survey , 2007, Comput. Methods Programs Biomed..