Evolutionary Instance Selection Algorithm based on Takagi-Sugeno Fuzzy Model

Lee, Sang-Hong; Lim, Joon S.

doi:10.12785/amis/080346

Cited by 2 publications

(2 citation statements)

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Section: Agent-based Population Learningmentioning

confidence: 99%

“…Hence, to solve the problem, some metaheuristics or other approximate algorithms are required. Numerous approaches to data reduction through instance selection have been based on using genetic or evolutionary algorithms (see, for example, [15,42,43]). In this paper, to enable dealing with huge datasets and to make the learning process more effective, it has been decided to apply the dual data reduction, that is, a reduction in the feature and instance spaces.…”

Section: The Proposed Approach To Learning Frommentioning

confidence: 99%

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An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques

Czarnowski

Jędrzejowicz

2018

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In the paper, several data reduction techniques for machine learning from big datasets are discussed and evaluated. The discussed approach focuses on combining several techniques including stacking, rotation, and data reduction aimed at improving the performance of the machine classification. Stacking is seen as the technique allowing to take advantage of the multiple classification models. The rotation-based techniques are used to increase the heterogeneity of the stacking ensembles. Data reduction makes it possible to classify instances belonging to big datasets. We propose to use an agent-based population learning algorithm for data reduction in the feature and instance dimensions. For diversification of the classifier ensembles within the rotation also, alternatively, principal component analysis and independent component analysis are used. The research question addressed in the paper is formulated as follows: does the performance of a classifier using the reduced dataset be improved by integrating the data reduction mechanism with the rotation-based technique and the stacking?

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Section: Agent-based Population Learningmentioning

confidence: 99%

Section: The Proposed Approach To Learning Frommentioning

confidence: 99%