K-CM: A new artificial neural network. Application to supervised pattern recognition

• A novel pattern recognition method (K-CM) is proposed.
• K-CM combines a neural network with a sample fuzzy profiling and k-NN classifier.
• Performance of K-CM was evaluated on ten different datasets.

Artificial neural networks can be currently considered as one of the most important emerging tools in multivariate analysis due to their ability to deal with non-liner complex systems.In this work, a recently proposed neural network, called K-Contractive Map (K-CM), is presented and its performance in classification is evaluated towards other well-known classification methods. K-CM exploits the non-linear variable relationships provided by the Auto-CM neural network to obtain a fuzzy profiling of the samples and then applies the k-NN classifier to evaluate the class membership of samples. The algorithm Training with Input Selection and Testing (TWIST) is applied prior to K-CM to perform training/test data splitting for model parameter optimization and validation. This novel classification strategy was evaluated on ten different datasets and the obtained results were generally satisfactory.