Multidimensional data is often feature space heterogeneous so that individual features have unequal importance in different sub areas of the feature space. This motivates to search for a technique that provides a strategic splitting of the instance space being able to identify the best subset of features for each instance to be classified. Our technique applies the wrapper approach where a classification algorithm is used as an evaluation function to differentiate between different feature subsets. In order to make the feature selection local, we apply the recent technique for dynamic integration of classifiers. This allows to determine which classifier and which feature subset should be used for each new instance. Decision trees are used to help to restrict the number of feature combinations analyzed. For each new instance we consider only those feature combinations that include the features present in the path taken by the new instance in the decision tree built on the whole feature set. We evaluate our technique on data sets from the UCI machine learning repository. In our experiments, we use the C4.5 algorithm as the learning algorithm for base classifiers and for We would like to thank the UCI machine learning repository of databases, domain theories and data generators for the data sets, and the machine learning library in C++ for the source code used in this study. We are grateful to the anonymous referees for their valuable comments and constructive criticism.

. One approach in classification tasks is to use machine learning

. Decision committee learning has demonstrated spectacular success in reducing classification error from learned classifiers. These techniques develop a classifier in the form of a committee of subsidiary classifiers. The combination of outputs is usually performed by majority vote. Voting, however, has a shortcoming. It is unable to take into account local expertise. When a new instance is difficult to classify, then the average classifier will give a wrong prediction, and the majority vote will more probably result in a wrong prediction. Instead of voting, dynamic integration of classifiers can be used, which is based on the assumption that each committee member is best inside certain subareas of the whole feature space. In this paper, the proposed dynamic integration technique is evaluated with AdaBoost and Bagging, the decision committee approaches which have received extensive attention recently. The comparison results show that boosting and bagging have often signific...

. In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classification result. Recently promising approaches using parallel and distributed computing have been presented. In this paper, we consider an approach that uses classifiers trained on a number of data subsets in parallel as in the arbiter meta-learning technique. We suggest that information is collected during the learning phase about the performance of the included base classifiers and arbiters and that this information is used during the application phase to select the best classifier dynamically. We evaluate our technique and compare it with the simple arbiter meta-learning using selected data sets from the UCI machine learning repository. The comparison results show that our dynamic meta-learning technique outperforms the arbiter metalearning significantly in some cases but further profound analysi...

Abstract. One of the important directions in improvement of the data-mining and knowledge discovery methods is the integration of multiple classification techniques. An integration technique should estimate and then select the most appropriate component classifiers from an ensemble of classifiers. We discuss an advanced dynamic integration technique with multiple classifiers as one variation of the stacked generalization method based on the assumption that each component classifier is the best inside some sub areas of the application domain. In the learning phase a performance matrix of each component classifier is derived and it is then used during the application phase to estimate the performance of each component classifier with new instances. 1.

In data mining, the selection of appropriate classifier to estimate some unknown attribute of a new instance has an essential role for the quality of results. Recently interesting approaches with parallel and distributed computing have been presented. In this paper we discuss an approach that uses classifiers trained on a number of data subsets in parallel as in the arbiter meta-learning technique. We suggest collecting the performance information of base classifiers and arbiters and the use of this information during the application phase to select the appropriate classifier dynamically. Despite of many open questions we are convinced that the dynamic selection approach suggested in this paper includes interesting characteristics that at least in some situations offer benefits in comparison with other meta-learning approaches. 1. Introduction Data mining is the process of finding previously unknown and potentially interesting patterns and relations in large databases [5]. A typical d...