Reducing multiclass to binary by coupling probability estimates
نویسنده
چکیده
This paper presents a method for obtaining class membership probability estimates for multiclass classification problems by coupling the probability estimates produced by binary classifiers. This is an extension for arbitrary code matrices of a method due to Hastie and Tibshirani for pairwise coupling of probability estimates. Experimental results with Boosted Naive Bayes show that our method produces calibrated class membership probability estimates, while having similar classification accuracy as loss-based decoding, a method for obtaining the most likely class that does not generate probability estimates.
منابع مشابه
Convex Optimization for Binary Classifier Aggregation in Multiclass Problems
Multiclass problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. Various methods, including all-pairs (APs), one-versus-all (OVA), and error correcting output code (ECOC), have been studied, to decompose multiclass problems into binary problems. However, little study has been made to optimal...
متن کاملBiomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability
Universal Nearest Neighbours (unn) is a classifier recently proposed, which can also effectively estimates the posterior probability of each classification act. This algorithm, intrinsically binary, requires the use of a decomposition method to cope with multiclass problems, thus reducing their complexity in less complex binary subtasks. Then, a reconstruction rule provides the final classifica...
متن کاملMulticlass Posterior Probability Twin SVM for Motor Imagery EEG Classification
Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this p...
متن کاملKernel Methods, Multiclass Classification and Applications to Computational Molecular Biology
Support Vector Machines for pattern recognition were initially conceived for the binary classification case. A common approach to address multi-classification problems with binary classifiers is that of reducing the multiclass problem to a set of binary sub-problems, and combine their predictions in order to obtain a multiclass prediction. Reduction schemes can be represented by Error Correctin...
متن کاملSolving Multiclass Learning Problems viaError - Correcting Output
Multiclass learning problems involve nding a deenition for an unknown function f (x) whose range is a discrete set containing k > 2 values (i.e., k \classes"). The deenition is acquired by studying collections of training examples of the form hx i ; f (x i)i. Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorit...
متن کامل