Finite State Machine Based Binary Classifier

In this work, a novel classifier which has the ability of making binary classifications by supervised learning is introduced. The proposed classifier generates a finite state machine which is derived from the dataset used for training. The states of this machine show the likelihood that the visiting samples belong to one of the classes concerned. Learning process is realized by recording the states mostly visited by the samples of a class. And the classification is done by examining the states visited by the sample being tested are mostly visited by the samples of which class during the training. In the realized experiments, it has been seen that the proposed classifier makes as successful classifications as artificial neural networks and binary classification trees. The main advantages of the proposed classifier are that it requires fewer iterations than neural networks and does not require rules such as splitting criterion as in binary decision trees. Its another advantage is that learning can be performed while data is flowing in one direction, without the need for a back-propagation method such as used in neural networks.