Data Mining with Computational Intelligence by Wang Lipo

By Wang Lipo

Discovering details hidden in information is as theoretically tough because it is virtually vital. With the target of studying unknown styles from facts, the methodologies of information mining have been derived from statistics, desktop studying, and synthetic intelligence, and are getting used effectively in program components comparable to bioinformatics, banking, retail, and so forth. Wang and Fu found in element the cutting-edge on how you can make the most of fuzzy neural networks, multilayer perceptron neural networks, radial foundation functionality neural networks, genetic algorithms, and help vector machines in such purposes. They specialise in 3 major info mining initiatives: facts dimensionality relief, category, and rule extraction. The publication is focused at researchers in either academia and undefined, whereas graduate scholars and builders of knowledge mining platforms also will benefit from the particular algorithmic descriptions.

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The 50 lowest NMSEs are kept for calculations of mean and standard deviation, which are then used for comparisons. The simulations indicate that the input points 1, 4, and 5 are consistently less important than other inputs (Fig. 5). Simulations are re-run after these less important inputs are eliminated. This results in a network of size 17:2:1 (seventeen inputs, two hidden neurons and one output neuron). 1 Wavelet MLP Neural Networks for Time-series Prediction 31 Fig. 5. Distribution of relative importance of 20 inputs for the wavelet MLP network with decomposition level one in one of the simulations, which is similar to the results in other simulations.

Dotted line is the actual data, whereas continuous line is the predicted data. 1 Wavelet Packet Multi-layer Perceptron Neural Networks This section describes the wavelet packet MLP (WP-MLP) and its application to time-series prediction [332]. Instead of decomposing the input using wavelets as in the wavelet MLP studied in the previous section, the WP-MLP is used as a feature extraction method to obtain time-frequency information. The WP-MLP has been successfully applied to classification of biomedical signals, images, and speech.

The results from the four algorithms are compared with other algorithms. In Chap. 8, a hybrid neural network predictor is described for protein secondary structure prediction (PSSP). The hybrid network is composed of the RBF neural network and the MLP neural network. Experiments show that the performance of the hybrid network has reached a comparable performance with the existing leading method. In Chap. , cancer diagnosis based on gene expression data and protein secondary structure prediction.

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