A differential evolution approach to dimensionality reduction for classification needs
Goran Martinović, Dražen Bajer, Bruno Zorić (2014)
International Journal of Applied Mathematics and Computer Science
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Goran Martinović, Dražen Bajer, Bruno Zorić (2014)
International Journal of Applied Mathematics and Computer Science
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Ireneusz Czarnowski, Piotr Jędrzejowicz (2011)
International Journal of Applied Mathematics and Computer Science
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The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning...
Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)
International Journal of Applied Mathematics and Computer Science
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Michał Woźniak, Bartosz Krawczyk (2012)
International Journal of Applied Mathematics and Computer Science
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This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier,...
Koychev, Ivan (2007)
Serdica Journal of Computing
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This paper addresses the task of learning classifiers from streams of labelled data. In this case we can face the problem that the underlying concepts can change over time. The paper studies two mechanisms developed for dealing with changing concepts. Both are based on the time window idea. The first one forgets gradually, by assigning to the examples weight that gradually decreases over time. The second one uses a statistical test to detect changes in concept and then optimizes the...
Mohammed Khabzaoui, Clarisse Dhaenens, El-Ghazali Talbi (2008)
RAIRO - Operations Research
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An important task of knowledge discovery deals with discovering association rules. This very general model has been widely studied and efficient algorithms have been proposed. But most of the time, only frequent rules are seeked. Here we propose to consider this problem as a multi-objective combinatorial optimization problem in order to be able to also find non frequent but interesting rules. As the search space may be very large, a discussion about different approaches is proposed...
Dumitrescu, D., Simon, Károly (2003)
Acta Universitatis Apulensis. Mathematics - Informatics
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Zou, Wenping, Zhu, Yunlong, Chen, Hanning, Sui, Xin (2010)
Discrete Dynamics in Nature and Society
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Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)
International Journal of Applied Mathematics and Computer Science
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Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency...