A Learning Algorithm with Boosting for Fuzzy Reasoning Model
Hiromi Miyajima, Noritaka Shigei, Shinya Fukumoto, N. Nakatsu · 2007
There have been proposed many learning algorithms for fuzzy reasoning models based on the steepest descend method. However, any learning algorithm known as a superior one does not always work well. This paper proposes a new learning algorithm with boosting. Boosting is a general method which attempts to boost the accuracy of any given learning algorithm. The proposed method consists of three sub-learners. The first sub-learner is constructed by performing the conventional learning algorithm with randomly selected data from given data space. The second sub-learner is constructed by performing the conventional learning algorithm with the data selected with equal probability from correctly and incorrectly learned data in the first learning. The third sub-learner is constructed with the data for which either the first or the second sub-learner is incorrectly learned. The output for any input data is given as decision by majority among the outputs of three sub-learners. That is, the method attempts to boost correctly learned data by learning incorrectly learned data repeatedly. In order to show the effectiveness of the proposed algorithm, numerical simulations are performed.