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This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training(More)
One central problem of information retrieval (IR) is to determine which documents are relevant and which are not to the user information need. This problem is practically handled by a ranking function which defines an ordering among documents according to their degree of relevance to the user query. This paper discusses work on using machine learning to(More)
Classi®cation is one of the important tasks in developing expert systems. Most of the previous approaches for classi®cation problem are based on classi®cation rules generated by decision trees. In this paper, we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. An adaptable incremental(More)
This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes. Two methods of(More)
This paper presents a learning scheme for data classification based on genetic programming. The proposed learning approach consists of an adaptive incremental learning strategy and distance-based fitness functions for generating the discriminant functions using genetic programming. To classify data using the discriminant functions effectively, the mechanism(More)
Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this paper, we propose an effective scheme for learning multi-category classifiers based on genetic programming. For a k-class classification problem, a training strategy called adaptable(More)
The classification problem is an important topic in knowledge discovery and machine learning. Traditional classification tree methods and their improvements have been discussed widely. This work proposes a new approach to construct decision trees based on discriminant functions which are learned using genetic programming. A discriminant function is a(More)
This paper proposes a novel architecture of genetic programming (GP) called layered genetic programming (LAYERGP) to generate new features of a given dataset. LAYERGP is a new architecture of multipopulation GP (MGP) that contains a number of layers each of which comprises a set of independent populations. New features are created by evaluating the best(More)
Knowledge discovery and data mining have become a hot research topic of late years. Classification is one of the most important problems in knowledge discovery. So many different classification algorithms have been developed for classifying data. In this paper, we present an effective scheme for classifying data with multi-category based on the technique of(More)
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