WiSARD4WEKA: WiSARD model in WEKA Data Mining Software

The WiSARD is a RAM-based neuron network working as an n-tuple classifier. A WiSARD is formed by as many discriminators as the number of classes it has to discriminate between. Each discriminator consists of a set of RAMs that, during the training phase, learn the occurrences of n-tuples extracted from the input binary vector (the retina). In the WiSARD model, n-tuples selected from the input binary vector are regarded as the "features" of the input pattern to be recognised. It has been demonstrated in literature that the randomness of feature extraction makes WiSARD more sensitive to detect global features than an ordered map which makes a single layer system sensitive to detect local features. More information and details about the WiSARD neural network model can be found in Aleksander and Morton's book Introduction to neural computing. While the WiSARD was originally conceived as a pattern recognition device mainly focusing on image processing domain, with ad hoc data transformation, this model can also be used successfully as multi-class classifier in machine learning domain. The WiSARD4WEKA package implements a multi-class classification method based on the WiSARD weightless neural model for the Weka machine learning toolkit. A data-preprocessing filter allows to exploit WiSARD neural model training/classification capabilities on multi-attribute numeric data making WiSARD overcome the restriction to binary pattern recognition. For more information on the WiSARD classifier implemented in the WiSARD4WEKA package.