Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets
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Data
2011-09-26
Autores
Ponti Jr., Moacir P.
Papa, João Paulo [UNESP]
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Resumo
The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.
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distributed combination of classifiers, Optimum-Path Forest classifier, pasting small votes, Combination of classifiers, Combining method, Combining schemes, Fast methods, Final decision, Fixed numbers, Forest classifiers, Learning procedures, Majority vote, Parallel or distributed processing, Random sample, Real data sets, Training algorithms, Training sets, Training subsets, Algorithms, Pattern recognition systems, Set theory
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6713 LNCS, p. 237-248.