Open AccessModification of the WaldBoost algorithm to improve the efficiency of solving pattern recognition problems in real-time
Author(s) -
А. Н. Чесалин,
С. Я. Гродзенский,
М. Ю. Нилов,
А. Н. Агафонов
Publication year - 2019
Publication title -
rossijskij tehnologičeskij žurnal/russian technological journal
Language(s) - English
Resource type - Journals
eISSN - 2782-3210
pISSN - 2500-316X
DOI - 10.32362/2500-316x-2019-7-5-20-29
Subject(s) - boosting (machine learning) , adaboost , sequential probability ratio test , algorithm , computer science , artificial intelligence , machine learning , random subspace method , pattern recognition (psychology) , mathematics , classifier (uml)
The implementation of the WaldBoost algorithm is considered, and its modification is proposed, which allows to significantly reduce the number of weak classifiers to achieve a given classification accuracy. The efficiency of the proposed algorithm is shown by specific examples. The paper studies modifications of compositions (ensembles) of algorithms for solving real-time pattern recognition problems. The aim of the study is to improve the known machine learning algorithms for pattern recognition using a minimum amount of time (the minimum number of used classifiers) and with a given accuracy of the results. We consider the implementation of the WaldBoost algorithm, which combines two algorithms: adaptive boosting of weak classifiers – AdaBoost (adaptive boosting), which has a high generalizing ability, and the sequential probability ratio test – SPRT (Wald test), which is the optimal rule of decision-making when distinguishing two hypotheses. It is noted that when using the WaldBoost, the values of the actual probability of classification errors, as a rule, are less than given because of the approximate boundaries of the SPRT, so that the classification process uses an excessive series of weak classifiers. In this regard, we propose a modification of the WaldBoost based on iterative refinement of the decision boundaries, which can significantly reduce the number of used weak classifiers required for pattern recognition with a given accuracy. The efficiency of the proposed algorithm is shown by specific examples. The results are confirmed by statistical modeling on several data sets. It is noted that the results can be applied in the refinement of other cascade classification algorithms.