Using an Interpolation Method to Make Classification Decision
Hua Jizho, Wang Jianguo · 2010
Pattern recognition techniques have been widely used. In this paper, we propose an interpolation method for making classification decision (AIMMCD). This method makes an interpolation of the class labels of the patterns of the training set for classifying a new pattern. Compared with conventional pattern recognition techniques, AIMMCD has several advantages. First, when we use AIMMCD to produce the class label for the test pattern, no any training procedure. This means that AIMMCD to be computationally efficient. Second, when AIMMCD predicts the class label for real-world data, it takes into account the information of the class labels of all the patterns from the training set in a reasonable way. Indeed, the algorithm assumes that the training sample close to a pattern will have much influence on the class prediction of this pattern and the training sample far from this pattern will have little influence. Third, though AIMMCD has a very simple form, it is directly applicable to not only two-class problems but also multi-class problems.