1,721,108 research outputs found
Combining Experts with Different Features for Classifying Clustered Microcalcifications in Mammograms
No full textIEEE Computer Society Pres
Exploiting AUC for Optimal Linear Combination of Dichotomizers
No full textThe combination of classifiers is an established technique to improve the classification performance. The possible combination rules proposed up to now generally try to decrease the classification error rate, which is a performance measure not suitable in many real situations and particularly when dealing with two-class problems. In this case, a good alternative is given by the area under the receiver operating characteristic curve (AUC), whose effectiveness in measuring the classification quality has been proved in many recent papers.
In this paper, we propose a method to achieve the optimal linear combination of two dichotomizers based on the maximization of the AUC of the resulting classification system. The effectiveness of the approach has been confirmed by the tests performed on standard datasets
Combining Experts with Different Features for Classifying Clustered Microcalcifications in Mammograms
No full textIEEE Computer Society Pres
Exploiting coding theory for classification: An LDPC-based strategy for multiclass-to-binary decomposition
Full text linkA powerful strategy for the classification of multiple classes is to create a classifier ensemble that decomposes the polychotomy into several dichotomies. The central issue when designing a multiclass-to-binary decomposition scheme is the definition of both the coding matrix and the decoding algorithm. In this study, we propose a new classification system based on low-density parity-check codes, which is a very effective class of binary block codes. The main idea is to exploit the algebraic properties of the codes to generate the codewords for the coding matrix and to define two decoding approaches, which allow us to detect and recover possible errors or rejects produced by the dichotomizers. Experiments based on benchmark datasets demonstrated that the proposed approach provides a statistically significant improvement in terms of the classification performance compared with state-of-the-art decomposition strategies
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