Class label altering fuzzy min-max network and its application to histopathology image database

Hyperbox classifier is efficiently implemented using fuzzy min max neural network, where the input patterns present in the training phase place a vital role. In the training phase, a set of hyperboxes are constructed which are used to classify a testing pattern. A better selection of input patterns of the training set will generate an efficient set of hyperboxes. Inappropriate selection of training samples leads to an erroneous set of hyperboxes, which will degrade the accuracy rate. Therefore, instead of using a single training set an extra training set, secondary training set, may be used to reshuffle the hyperboxes generated during the primary training set. In this paper, we have used a secondary training set to update the hyperboxes generated during the primary training phase. When a secondary training set is used, two cases are arised. First, change the class label of some inefficient hyperboxes created during the primary training phase. Second, fix the class label of efficient hyperboxes to the class allotted during the primary training set. By using the above secondary training set mechanism, a novel class label altering fuzzy min max (CLAFMM) network is proposed to alter the class labels depending on the secondary training set. Experimental results prove that the proposed approach provides more accuracy rate than the FMM, Enhanced FMM and K-nearest FMM. Simultaneously, the proposed approach reduces the complexity of the network by reducing the number of hyperboxes generated by the above said state-of-the-art methods. The proposed method is also applied to the breast cancer histopathological images to identify the best magnifying factor for classification.

Automated summary

The Hyperbox classifier is efficiently implemented using a fuzzy min-max neural network. In the training phase, a set of hyperboxes is constructed based on the input patterns which play a vital role in classification. The use of a secondary training set helps in updating and improving the efficiency of hyperboxes, ultimately leading to higher accuracy rates in classification tasks.