Data Mining with Computational Intelligence (Advanced by Lipo Wang

By Lipo Wang

Discovering info hidden in information is as theoretically tough because it is essentially vital. With the target of getting to know unknown styles from info, the methodologies of knowledge mining have been derived from facts, computer studying, and synthetic intelligence, and are getting used effectively in program components equivalent to bioinformatics, banking, retail, etc. 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 aid vector machines in such purposes. They concentrate on 3 major facts mining initiatives: information dimensionality relief, type, and rule extraction. The booklet is concentrated at researchers in either academia and undefined, whereas graduate scholars and builders of information mining platforms also will make the most of the distinctive algorithmic descriptions.

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Data dimensionality is reduced based on attribute ranking results. Data dimensionality reduction is then performed by combining the SCM method and RBF classifiers. In the DDR method, there are a fewer number of candidate feature subsets to be inspected compared with other methods, since attribute importance is ranked first by the SCM method. The size of a data set is reduced and the architecture of the RBF classifier is simplified. Experimental results show the advantages of the DDR method. In Chap.

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. , (1) an input with a tapped delay line with p delays, (2) wavelet packet decomposition, and (3) an MLP. The output x ˆ(n + 1) is the value of the time-series at time n + 1 and is assumed to be a function of the values of the time-series at p previous time steps.

26), which represents the error between the desired and the actual outputs. If the layer concerned is one of the hidden layers, δ needs to be calculated with some propagating rule, such as Eq. 28). The most popular non-linear function for a neuron is the sigmoid function. 2 Cost-sensitive Back-propagation In conventional back-propagation, errors made with respect to different patterns are assumed to be the same, as shown in the cost function given by Eq. 23). 30) µ,i where λµ is a cost-dependent factor.

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