Extended fuzzy hyperline-segment neural network with classification rule extraction

The hyperline segment layer of fuzzy hyperline segment neural network (FHLSNN) consists of number of n- dimensional hyperline segments. Each hyperline segment has the two end points defined in terms of continuous attribute values and an associated membership function. The membership function processes the continuous attribute values and gives the membership of each input pattern to the hyperline segments. These end points and membership function are defined only in terms of continuous attributes and cannot process discrete attributes. But the real world data comprises both continuous and discrete attributes. Due to this original FHLSNN and its ancestor versions cannot be applied to these types of data. Also, justification of the classification decision given by these fuzzy min-max neural networks is required to be obtained to make them more applicable to the real world applications. The objective of the proposed extended fuzzy hyperline segment neural network (EFHLSNN) is to solve these two problems. In the EFHLSNN, each hyperline segment has two end points defined not only in terms of continuous attributes but also the set of binary strings defined for discrete attributes. The membership function and expansion condition of the original FHLSNN are modified to process both continuous and discrete attribute values. The proposed EFHLSNN model is applied to eight different benchmark datasets. The experimental results show that the proposed model gives very good accuracy and the compact rule set that justifies the classification decision of the EFHLSNN and thus, giving rise to good rule fidelity.