Using Ant Programming Guided by Grammar for Building Rule-Based Classifiers
ABSTRACT The extraction of comprehensible knowledge is one of the major challenges in many domains. In this paper, an ant programming (AP) framework, which is capable of mining classification rules easily comprehensible by humans, and, therefore, capable of supporting expert-domain decisions, is presented. The algorithm proposed, called grammar based ant programming (GBAP), is the first AP algorithm developed for the extraction of classification rules, and it is guided by a context-free grammar that ensures the creation of new valid individuals. To compute the transition probability of each available movement, this new model introduces the use of two complementary heuristic functions, instead of just one, as typical ant-based algorithms do. The selection of a consequent for each rule mined and the selection of the rules that make up the classifier are based on the use of a niching approach. The performance of GBAP is compared against other classification techniques on 18 varied data sets. Experimental results show that our approach produces comprehensible rules and competitive or better accuracy values than those achieved by the other classification algorithms compared with it.
- Data Science Journal. 01/2008; 7:76-87.
- Journal of Machine Learning Research. 01/2006; 7:1-30.
- [show abstract] [hide abstract]
ABSTRACT: The genetic programming (GP) paradigm is a functional approach to inductively forming programs. The use of natural selection based on a fitness function for reproduction of the program population has allowed many problems to be solved that require a non-fixed representation. Attempts to extend GP have focussed on typing the language to restrict crossover and to ensure legal programs are always created. We describe the use of a context free grammar to define the structure of the initial language and to direct crossover and mutation operators. The use of a grammar to specify structure in the hypothesis language allows a clear statement of inductive bias and control over typing. Modifying the grammar as the evolution proceeds is used as an example of learnt bias. This technique leads to declarative approaches to evolutionary learning, and allows fields such as incremental learning to be incorporated under the same paradigm. 1 Introduction The Genetic Programming par...06/1999;