Feature-based ordering algorithm for data presentation of fuzzy ARTMAP ensembles.

This brief presents a new ordering algorithm for data presentation of fuzzy ARTMAP (FAM) ensembles. The proposed ordering algorithm manipulates the presentation order of the training data for each member of a FAM ensemble such that the categories created in each ensemble member are biased toward the vector of the chosen input feature. Diversity is created by varying the training presentation order based on the ascending order of the values from the most uncorrelated input features. Analysis shows that the categories created in two FAMs are compulsively diverse when the chosen input features used to determine the presentation order of the training data are uncorrelated. The proposed ordering algorithm was tested on 10 classification benchmark problems from the University of California, Irvine, machine learning repository and a cervical cancer problem as a case study. The experimental results show that the proposed method can produce a diverse, yet well generalized, FAM ensemble.