Navegando por Palavras-chave "hyper-heuristics"

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    Uma abordagem multiobjetiva para construção automática de algoritmos de indução de árvores de decisão
    (Universidade Federal de São Paulo (UNIFESP), 2015-02-23) Silva, Melis Mendes [UNIFESP]; Basgalupp, Marcio Porto [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    Decision tree induction is one of the most employed methods to extract knowledge from data, as the representation of knowledge is very intuitive and easily understandable by humans. A successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. After recent breakthroughs in the automatic design of machine learning algorithms, was proposed a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. In this work, this approach was expanded, making the fitness function, which previously worked with only one goal in multiobjective function. In this context, it was used two techniques for optimizing multi-objective fitness: a weighted formula and the lexicographical technique. Experiments will be conducted in 20 public data sets to assess the performance of the new version of HEAD-DT, and we compare it to the traditional decision-tree algorithms C4.5, CART in addition to the original version of HEAD- DT algorithm. Results show that the multi-objective version of HEAD-DT is able to generate promising algorithms when compared to both previous version of HEAD-DT, C4.5 and CART regarding predictive accuracy, F-Measure and complexity (number of nodes). Therefore, this work presents the first efforts to transform the HEAD-DT in a multiobjective algorithm, that is, able to guide the process of evolution by two or more goals.
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    Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms
    (Mit Press, 2013-11-01) Barros, Rodrigo C.; Basgalupp, Marcio P. [UNIFESP]; Carvalho, Andre C. P. L. F. de; Freitas, Alex A.; Universidade de São Paulo (USP); Universidade Federal de São Paulo (UNIFESP); Univ Kent
    This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. the automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. the proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. the algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.
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    Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets
    (Ieee-inst Electrical Electronics Engineers Inc, 2014-12-01) Barros, Rodrigo C.; Basgalupp, Márcio Porto [UNIFESP]; Freitas, Alex A.; Carvalho, Andre C. P. L. F. de; Pontificia Univ Catolica Rio Grande do Sul; Universidade de São Paulo (USP); Universidade Federal de São Paulo (UNIFESP); Univ Kent
    Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. the most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. in this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.