Displaying similar documents to “Self-adaptation of parameters in a learning classifier system ensemble machine”

Experiments with two Approaches for Tracking Drifting Concepts

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...

Combined classifier based on feature space partitioning

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,...

Comparison of speaker dependent and speaker independent emotion recognition

Jan Rybka, Artur Janicki (2013)

International Journal of Applied Mathematics and Computer Science

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This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector...

Egipsys: An enhanced gene expression programming approach for symbolic regression problems

Heitor Lopes, Wagner Weinert (2004)

International Journal of Applied Mathematics and Computer Science

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This paper reports a system based on the recently proposed evolutionary paradigm of gene expression programming (GEP). This enhanced system, called EGIPSYS, has features specially suited to deal with symbolic regression problems. Amongst the new features implemented in EGIPSYS are: new selection methods, chromosomes of variable length, a new approach to manipulating constants, new genetic operators and an adaptable fitness function. All the proposed improvements were tested separately,...

Diagnosing corporate stability using grammatical evolution

Anthony Brabazon, Michael O'Neill (2004)

International Journal of Applied Mathematics and Computer Science

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Grammatical Evolution (GE) is a novel data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to construct a series of models for the prediction of bankruptcy, employing information drawn from financial statements. Unlike prior studies in this domain, the raw financial information is not preprocessed into pre-determined financial ratios. Instead, the ratios...

A biologically inspired approach to feasible gait learning for a hexapod robot

Dominik Belter, Piotr Skrzypczyński (2010)

International Journal of Applied Mathematics and Computer Science

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The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned...

A multistrategy approach for digital text categorization.

María Dolores Castillo, José Ignacio Serrano (2005)

Mathware and Soft Computing

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The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents,...