Poker hand classification

Artificial Neural Network (ANN) forms a useful tool in pattern recognition tasks. Collection of five, eight or more cards in a cards game are normally called poker hands. There are various poker variations, each with different poker hands ranking. In the present paper, an attempt is made to solve poker hand classification problem using different learning paradigms and architectures of artificial neural network: multi-layer feed-forward Backpropagation (supervised) and self-organizing map (un-supervised). Poker data set is touted to be a difficult dataset for classification algorithms. Experimental results are presented to demonstrate the performance of the proposed system. The paper also aims to suggest about training algorithms and training parameters that must be chosen in order to solve poker hand classification problem using neural network model. As neural networks are the most convenient tools for handling complicated data sets with real values, one of the most important objectives of the paper is to explain how a neural network can also be used successfully for classification kind of problems involving categorical attributes. The proposed model succeeded in classification of poker hands with 94% classification accuracy.