The Experts below are selected from a list of 2100 Experts worldwide ranked by ideXlab platform

Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.

  • gp coach genetic programming based learning of compact and accurate fuzzy rule based classification systems for high dimensional problems
    Information Sciences, 2010
    Co-Authors: F J Berlanga, Mj. Del Jesus, Antonio J Rivera, Francisco Herrera
    Abstract:

    In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns Disjunctive Normal Form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good perFormance in terms of accuracy and interpretability.

  • evolutionary algorithms for subgroup discovery in e learning a practical application using moodle data
    Expert Systems With Applications, 2009
    Co-Authors: Cristobal Romero, Mj. Del Jesus, Pedro Gonzalez, Sebastian Ventura, Francisco Herrera
    Abstract:

    This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle course management system, a case study of the University of Cordoba, Spain. The objective is to obtain rules which describe relationships between the student's usage of the different activities and modules provided by this e-learning system and the final marks obtained in the courses. We use an evolutionary algorithm for the induction of fuzzy rules in canonical Form and Disjunctive Normal Form. The results obtained by different algorithms for subgroup discovery are compared, showing the suitability of the evolutionary subgroup discovery to this problem.

  • a novel genetic cooperative competitive fuzzy rule based learning method using genetic programming for high dimensional problems
    2008 3rd International Workshop on Genetic and Evolving Systems, 2008
    Co-Authors: F J Berlanga, Mj. Del Jesus, Francisco Herrera
    Abstract:

    In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns Disjunctive Normal Form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good perFormance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal.

  • Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
    IEEE Transactions on Fuzzy Systems, 2007
    Co-Authors: Mj. Del Jesus, Patricia González González, Francisco Herrera, Mikel Mesonero
    Abstract:

    This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in Disjunctive Normal Form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable Form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragon, Spain, in which it is necessary to extract automatically relevant and interesting inFormation that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.

  • a genetic programming based approach for the learning of compact fuzzy rule based classification systems
    Lecture Notes in Computer Science, 2006
    Co-Authors: F J Berlanga, Mj. Del Jesus, Maria Jose Gacto, Francisco Herrera
    Abstract:

    In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where Disjunctive Normal Form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification perFormance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.

Mj. Del Jesus - One of the best experts on this subject based on the ideXlab platform.

  • gp coach genetic programming based learning of compact and accurate fuzzy rule based classification systems for high dimensional problems
    Information Sciences, 2010
    Co-Authors: F J Berlanga, Mj. Del Jesus, Antonio J Rivera, Francisco Herrera
    Abstract:

    In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns Disjunctive Normal Form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good perFormance in terms of accuracy and interpretability.

  • evolutionary algorithms for subgroup discovery in e learning a practical application using moodle data
    Expert Systems With Applications, 2009
    Co-Authors: Cristobal Romero, Mj. Del Jesus, Pedro Gonzalez, Sebastian Ventura, Francisco Herrera
    Abstract:

    This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle course management system, a case study of the University of Cordoba, Spain. The objective is to obtain rules which describe relationships between the student's usage of the different activities and modules provided by this e-learning system and the final marks obtained in the courses. We use an evolutionary algorithm for the induction of fuzzy rules in canonical Form and Disjunctive Normal Form. The results obtained by different algorithms for subgroup discovery are compared, showing the suitability of the evolutionary subgroup discovery to this problem.

  • a novel genetic cooperative competitive fuzzy rule based learning method using genetic programming for high dimensional problems
    2008 3rd International Workshop on Genetic and Evolving Systems, 2008
    Co-Authors: F J Berlanga, Mj. Del Jesus, Francisco Herrera
    Abstract:

    In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns Disjunctive Normal Form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good perFormance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal.

  • Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
    IEEE Transactions on Fuzzy Systems, 2007
    Co-Authors: Mj. Del Jesus, Patricia González González, Francisco Herrera, Mikel Mesonero
    Abstract:

    This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in Disjunctive Normal Form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable Form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragon, Spain, in which it is necessary to extract automatically relevant and interesting inFormation that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.

  • a genetic programming based approach for the learning of compact fuzzy rule based classification systems
    Lecture Notes in Computer Science, 2006
    Co-Authors: F J Berlanga, Mj. Del Jesus, Maria Jose Gacto, Francisco Herrera
    Abstract:

    In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where Disjunctive Normal Form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification perFormance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.

F J Berlanga - One of the best experts on this subject based on the ideXlab platform.

  • gp coach genetic programming based learning of compact and accurate fuzzy rule based classification systems for high dimensional problems
    Information Sciences, 2010
    Co-Authors: F J Berlanga, Mj. Del Jesus, Antonio J Rivera, Francisco Herrera
    Abstract:

    In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns Disjunctive Normal Form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good perFormance in terms of accuracy and interpretability.

  • a novel genetic cooperative competitive fuzzy rule based learning method using genetic programming for high dimensional problems
    2008 3rd International Workshop on Genetic and Evolving Systems, 2008
    Co-Authors: F J Berlanga, Mj. Del Jesus, Francisco Herrera
    Abstract:

    In this contribution, we present GP-COACH, a novel GFS based on the cooperative-competitive learning approach, that uses genetic programming to code fuzzy rules with a different number of variables, for getting compact and accurate rule bases for high dimensional problems. GP-COACH learns Disjunctive Normal Form rules (generated by means of a context-free grammar) and uses a token competition mechanism to maintain the diversity of the population. It makes the rules compete and cooperate among themselves, giving out a compact set of fuzzy rules that presents a good perFormance. The good results obtained in an experimental study involving several high dimensional classification problems support our proposal.

  • a genetic programming based approach for the learning of compact fuzzy rule based classification systems
    Lecture Notes in Computer Science, 2006
    Co-Authors: F J Berlanga, Mj. Del Jesus, Maria Jose Gacto, Francisco Herrera
    Abstract:

    In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to an FRBCS with a rule base with a high cardinality. In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where Disjunctive Normal Form (DNF) rules compete and cooperate among themselves in order to obtain an understandable and compact set of fuzzy rules, which presents a good classification perFormance with high dimensionality problems. This proposal uses a token competition mechanism to maintain the diversity of the population. The good results obtained with several classification problems support our proposal.

Andrew Y Y Tan - One of the best experts on this subject based on the ideXlab platform.

  • approximating boolean functions with Disjunctive Normal Form
    arXiv: Computational Complexity, 2020
    Co-Authors: Yunhao Yang, Andrew Y Y Tan
    Abstract:

    The theorem states that: Every Boolean function can be $\epsilon -approximated$ by a Disjunctive Normal Form (DNF) of size $O_{\epsilon}(2^{n}/\log{n})$. This paper will demonstrate this theorem in detail by showing how this theorem is generated and proving its correctness. We will also dive into some specific Boolean functions and explore how these Boolean functions can be approximated by a DNF whose size is within the universal bound $O_{\epsilon}(2^{n}/\log{n})$. The Boolean functions we interested in are: Parity Function: the parity function can be $\epsilon-approximated$ by a DNF of width $(1 - 2\epsilon)n$ and size $2^{(1 - 2\epsilon)n}$. Furthermore, we will explore the lower bounds on the DNF's size and width. Majority Function: for every constant $1/2 < \epsilon < 1$, there is a DNF of size $2^{O(\sqrt{n})}$ that can $\epsilon-approximated$ the Majority Function on n bits. Monotone Functions: every monotone function f can be $\epsilon-approximated$ by a DNF g of size $2^{n - \Omega\epsilon(n)}$ satisfying $g(x) \le f(x)$ for all x.

Mikel Mesonero - One of the best experts on this subject based on the ideXlab platform.

  • Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
    IEEE Transactions on Fuzzy Systems, 2007
    Co-Authors: Mj. Del Jesus, Patricia González González, Francisco Herrera, Mikel Mesonero
    Abstract:

    This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in Disjunctive Normal Form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable Form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragon, Spain, in which it is necessary to extract automatically relevant and interesting inFormation that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.