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 traditional Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.

The sixty three revised complete papers awarded 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, tender computing, bioinformatics in addition to applications.

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Read or Download Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers PDF

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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

Example text

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.

In the future, we will look into incorporating ideas from them, as well as from evolutionary and multiobjective approaches such as those in [6,7,8]. For this paper though, we use a non-evolving, fully connected MLP, and use the more traditional gradient-based training at the core. Early stopping will be involved, in a way. In Sect. 2 we motivate and describe a heuristic training algorithm that operates using the ideas of cross-validation, brute-force parameter search, regularization, and early stopping.

First of all, an attempt can be made to acquire and fill in the missing data. This however can be time consuming, difficult, expensive or sometimes not even possible. Another possibility is to revise the data set and delete all the impaired instances or even attributes (this approach is sometimes called complete case analysis). When the data are valuable, this is unfortunately not feasible. The last solution is to impute the missing values using a proper method. The imputation approach makes it possible to avoid deleting possibly useful information on one side, but poses a threat of introducing errors into the data set on the other.

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