By Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)
This publication constitutes the completely refereed post-proceedings of the ninth overseas convention on Adaptive and ordinary Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.
The sixty three revised complete papers offered 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, tender computing, bioinformatics in addition to applications.
Read Online 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
This will require performing tests on a number of diﬀerent models and data sets with a larger gradation of incompleteness levels. To sum up, the results show that the imputation strategy found by the algorithm outperforms individual strategies used for the same data sets. Not only, the improvement of on average 73% in MSE rates has been obtained, but also the minimal improvement was 45%. Therefore, the algorithm helps to address the need for model-driven data imputation. 6 Summary Method vectors may lead to much better results in data imputation.
Wine: classifying wine cultivors; we have brieﬂy tried 13-5-4-3 and 13-100-3 nets on this task; without artiﬁcial tampering this looks a bit too “easy” for benchmarking. – PenDigits: the pen digit task with diﬀerent net architectures has been tried; it is the only one to be present in this paper. – Thyroid (ANN): the hypothyroid diagnosis task is being used, also with diﬀerent net architectures. More data sets will be tried later. Results of the benchmarking are still indecisive, and we postpone their presentation to a follow-up article.
MLP has been used to build a model using each data set. Java Neural Network Simulator (JNNS) was used to train a neural network for each of our test data sets. Having all the neural networks, data sets for the genetic algorithm were prepared. Values from 25% and 50% of the rows from the testing parts of Iris and Wisconsin data sets were removed. The Votes set is already incomplete. The obtained incomplete sets were used as the working sets for the rest of our tests. The objective of the algorithm was to minimize the neural network mean square errors achieved on the imputed working set.