Computação Evolutiva para a Construção de Regras de Conhecimento com Propriedades Específicas / Evolutionary Computing for Knowledge Rule Construction with Specific Properties

AUTOR(ES)
DATA DE PUBLICAÇÃO

2007

RESUMO

Most symbolic machine learning approaches use if-then know-ledge rules as the description language in which the learned knowledge is expressed. The aim of these learners is to find a set of classification rules that can be used to predict new instances that have not been seen by the learner before. However, these sorts of learners take into account the rule interaction problem, which consists of evaluating the quality of the set of rules (classifier) as a whole, rather than evaluating the quality of each rule in an independent manner. Thus, as classifiers aim at good precision to classify unseen instances, they tend to neglect other desirable properties of knowledge rules, such as the ability to cause surprise or bring new knowledge to the domain specialist. In this work, we are interested in building knowledge rules with specific properties in an isolated manner, i.e. not considering the rule interaction problem. To this end, we propose an evolutionary approach where each individual of the algorithm population represents a single rule and the specific properties are encoded as rule quality measure, a set of which can be freely selected by the domain specialist. The proposed evolutionary algorithm uses a rich structure for individual representation which enables one to consider a great variety of evolutionary operators. The algorithm uses a ranking-based multi-objective fitness function that considers more than one rule evaluation measure concomitantly into a single objective. As experimentation plays an important role in this sort of work, in order to evaluate our proposal we have implemented the Evolutionary Computing Learning Environment --- ECLE --- which is a framework to evaluate the evolutionary algorithm in different scenarios. Furthermore, the ECLE has been implemented taking into account future development of new evolutionary operators. The ECLE is integrated into the DISCOVER project, a major research project under constant development in our laboratory for automatic knowledge acquisition and analysis. Experimental analysis of the evolutionary algorithm to construct knowledge rules with specific properties, which can also be considered an important form of intelligent data analysis, was carried out using ECLE. Results show the suitability of our proposal

ASSUNTO(S)

descoberta de conhecimento knowledge discovery computação evolutiva knowledge rules evolutionary computing regras de conhecimento

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