Showing papers by "Jesús Alcalá-Fdez published in 2010"

Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules

Abstract: DataMining is most commonly used in attempts to induce association rules from transaction data which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Most conventional studies are focused on binary or discrete-valued transaction data, however the data in real-world applications usually consists of quantitative values. In the last years, many researches have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper, we present a study of three genetic association rules extraction methods to show their effectiveness for mining quantitative association rules. Experimental results over two real-world databases are showed.

A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems

Abstract: Service robots will play an increasing and more important role in the society in the next years. One of the main challenges is to endow robots with enough autonomy to operate on real environments. To reach that goal, the design of controllers to solve simple tasks must be automatized. Engineers look for learning algorithms that are general, robust, require low expertise knowledge, and generate controllers that can run on the real robot without any tuning stage. In this paper, a framework to learn behaviors (controllers) in mobile robotics, fulfilling the previous requirements, has been used. The framework is based on two modules: dataset generation and a data-driven evolutionary-based learning algorithm to obtain fuzzy controllers. Nevertheless, the design of a fuzzy controller still requires the selection of the type of learning algorithm, and also to choose the value of some design parameters. In this paper we present an exhaustive study on a set of evolutionary-based data-driven learning algorithms, for learning fuzzy controllers in mobile robotics, that cover a wide range of the accuracy/interpretability trade-off. The study has also evaluated the influence of the values of all the design parameters over accuracy and interpretability. The objective is to analyze the performance of the different algorithms for the design of behaviors in mobile robotics, and to extract some general rules that can help in the process to design new behaviors. The analysis comprises two different behaviors (wall-following and moving object following) and more than 450 tests, both in simulation and on a Pioneer II AT robot. Results have shown very good performances in complex and realistic conditions for the different combinations of algorithms and parameters.

Genetic tuning of a laser pointer environment control device system for handicapped people with fuzzy systems

Abstract: In this paper we present a new approach for laser-based environment device control systems by laser pointer for handicapped people. The paper proposes the design of a Fuzzy Rule Base System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs, i.e., the detection of a false laser spot (since this could lead to dangerous situations). To this end, Genetic Fuzzy Systems have also been employed for improving the laser spot system detection thus reducing the system false offs, that is the main objective in this problem. The system presented in this paper, using a Fuzzy Rule Base System adjusted by a Genetic Algorithm, shows a better success rate, and the most important thing, the not desired false offs are completely avoided.

A fuzzy associative classification system with genetic rule selection for high-dimensional problems

Abstract: The learning of Fuzzy Rule-Based Classification Systems for High-Dimensional problems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. In this work, we propose a fuzzy association rule-based classification method with genetic rule selection for high-dimensional problems to obtain an accurate and compact fuzzy rule-based classifier with low computational cost. The results obtained from the comparison with other two genetic fuzzy systems over nine real-world datasets with different characteristics show the effectiveness of the proposed approach.

Introduction to the Experimental Design in the Data Mining Tool KEEL

Abstract: KEEL is a Data Mining software tool to assess the behaviour of evolutionary learning algorithms in particular and soft computing algorithms in general for different kinds of Data Mining problems including as regression, classification, clustering, pattern mining and so on. It allows us to perform a complete analysis of some learning model in comparison to existing ones, including a statistical test module for comparison. In this chapter the authors will provide a complete description of KEEL, the kind of problems and algorithms implemented, and they will present a case of study for showing the experimental design and statistical analysis that they can do with KEEL. J. Alcalá-Fdez University of Granada, Spain I. Robles University of Granada, Spain F. Herrera University of Granada, Spain S. García University of Jaén, Spain M.J. del Jesus University of Jaén, Spain L. Sánchez University of Oviedo, Spain E. Bernadó-Mansilla University Ramon Llull, Spain A. Peregrín University of Huelva, Spain S. Ventura University of Córdoba, Spain DOI: 10.4018/978-1-61520-757-2.ch001