By Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)
This e-book constitutes the completely refereed post-proceedings of the ninth overseas convention on Adaptive and normal Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.
The sixty three revised complete papers awarded have been rigorously 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.
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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Additional info for Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers
K¨ arkk¨ ainen We are aware of other alternative heuristic methods to overcome the overﬁtting issue, such as network growing and pruning (or other ways of evolving the architecture), and early stopping by validation . 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.
It has been based on the evolution of imputation strategies built using both non-parametric and parametric imputation methods. Genetic algorithms and multilayer perceptrons have been applied to develop a framework for constructing the imputation strategies addressing multiple incomplete attributes. Furthermore we evaluate imputation methods in the context of not only the data they are applied to, but also the model using the data. The accuracy of classiﬁcation on data sets completed using obtained imputation strategies has been described.
Moreover, eM (D) denotes the mean absolute error of model M on data set D. Furthermore DV is the data set created from D by ﬁlling in all the missing values using methods from vector V . Using the above notation, the best method vector V ∗ = [m1 , m2 , . . , mn ] is deﬁned as follows: eM (DV ∗ ) = min eM (DV ) (1) V Equation 1 holds when all the methods in Γ are non-parametric, however some imputation methods do have parameters. In such situations not only the method vectors have to be found, but also their parameters.