Artificial Intelligence Applications and Innovations: 8th by Pedro Alves Nogueira, Luís Filipe Teófilo (auth.), Lazaros

By Pedro Alves Nogueira, Luís Filipe Teófilo (auth.), Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos (eds.)

This ebook constitutes the refereed court cases of the eighth IFIP WG 12.5 foreign convention on synthetic Intelligence purposes and suggestions, AIAI 2012, held in Halkidiki, Greece, in September 2012. The forty four revised complete papers and five revised brief papers provided have been rigorously reviewed and chosen from ninety eight submissions. The papers are geared up in topical sections on ANN-classification and trend popularity, optimization - genetic algorithms, synthetic neural networks, studying and mining, fuzzy good judgment, class - development reputation, multi-agent structures, multi-attribute DSS, clustering, image-video category and processing, and engineering functions of AI and synthetic neural networks.

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Extra info for Artificial Intelligence Applications and Innovations: 8th IFIP WG 12.5 International Conference, AIAI 2012, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part I

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The remainder of the paper is organized as follows. In section 2 the proposed method is shown in detail followed by the results and discussion in section 3. Conclusions are presented in section 4. 2 Multi-classify Hybrid Multilayered Perceptron Network It is well known [5] that modelling a linear framework using a standard nonlinear MLP network is not a good decision. For this reason a hybrid version of the MLP network was introduced known as the HMLP network. This involved adding linear connections linking the input layer directly to the output layer without going through the hidden layer, an approach which proved to significantly improve performance.

According to this approach in order to select the features the sum of the squared weights is calculated over the K binary classifications as shown in the following equation. Jj = (w ) K 1 K k j 2 . (1) k =1 In (1) Jj denotes the cost for not selecting feature j, w j k denotes the separating hyperplane’s weight that corresponds to the jth feature and the binary classifier for the kth class, whereas K denotes the number of different classes. Discriminative Function Pruning Analysis. The basic idea of the DFPA algorithm [8] is to learn the SVM discriminative function from training data using all input variables available first, and then perform pruning analysis in order to select feature subset.

These results are also supported by Mat-Isa et al. who, in their research, found that the performance of the RBF family of networks cannot be better than that of the MLP family [1]. The results in Tables 2 and 3 also indicate that the MCHMLP offers superior performance however the difference in performance is not as clear as in Table 1. In the case of Liver Disorder (Table 2) and Pima Indian Diabetes (Table 2) problem occurs when there is a large overlap between two groups in the datasets. The datasets are divided into groups A and B but, parts of the data belong to either group A or group B.

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