Download Adaptive and Natural Computing Algorithms: 9th International by Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, PDF

By Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)

This ebook constitutes the completely refereed post-proceedings of the ninth overseas convention on Adaptive and usual Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.

The sixty three revised complete papers offered have been conscientiously reviewed and chosen from a complete of 112 submissions. The papers are prepared in topical sections on impartial networks, evolutionary computation, studying, gentle computing, bioinformatics in addition to applications.

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Extra info for Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers

Sample text

Using the known pairs (xi , yi ) as examples, we aim to train a computer system to associate vectors with a corresponding class. For our current purposes, any vector y ∈ RC is converted to an exact class representative by choosing c equal to smallest k for which (y)k ≥ (y)j for all j ∈ {1, . . , selection of the index of the largest component, or, in the case of equality, the one with the smallest index. This conversion works fine for vectors that are already approximately close to the encoded class prototype.

The error rate achieved when using these imputation strategies is significantly lower than the error rate obtained when completing the data set by the best involved individual imputation method. The proposed framework allows to adopt multiple imputation methods used to deal with incompleteness of individual attributes. Not only the combination of them, but also their parameters can be set by evolution. Moreover, the framework construction allows to use different decision models. Any model, based on neural networks or other AI techniques that is applied to solve the classification or prediction problem can be included in the framework.

Algorithm 1 depicts the test procedure. 5 Results The results of the tests are summarised in Tab. 2. All the values have been obtained on the testing set. The set was used neither to drive the evolution process, nor to train the MLP model. eV stands for average MSE of the neural network on the data set filled in with method vectors. eS denotes average MSE of the neural network on the data set filled in with single method used to impute all the missing values in all incomplete attributes. What should be emphasised, the set of individual methods that are investigated in the latter case, contains all the methods that can be used in method vectors.

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