Intelligent Data Analysis: An International Journal

The problem of feature subset selection can be defined as the selection of a relevant subset of features which allows a learning algorithm to induce small high-accuracy models. This problem is of primary important because irrelevant and redundant features may degrade the learner speed, especially in the context of high dimensionality, and reduce both the accuracy and comprehensibility of the induced model. Two main approaches have been developed, the first one is algorithm-independent (filter approach) which considers only the data, when the second approach which is algorithm-dependent takes into account both the data and a given learning algorithm (wrapper approach). Recent work was developed to study the interest of the rough set theory and more particularly its notions of reducts and core to deal with the problem of feature subset selection. Different methods were proposed to select features using both the core and the reduct concepts, whereas other researches show that useful feature subsets do not necessarily contain all features in cores. In this paper, we underline the fact that rough set theory is concerned with deterministic analysis of attribute dependencies which are at the basis of the two notions of reduct and core. We extend the notion of dependency which allows to find both deterministic and non-deterministic dependencies. A new notion of strong reducts is then introduced and leads to the definition of strong feature subsets (SFS). The interest of SFS is illustrated by the improvement of the accuracy of C4.5 on real-world datasets. Our study shows that generally the highest-accuracy-subset is not the best one as regards to the filter criteria. The highest accuracy subset is found by the new approach with minimum cost. The contribution of this work is four folds : (1) analysis of feature subset selection in the rough sets context, (2) introduction of new definitions based on a generalized rough set theory, i.e., α-RST, (3) reformulation of the selection problem, (4) description of a hybrid method combining combining both the filter and the wrapper approaches.

Feature selection is a central problem in data analysis that have received a significant amount of attention from several disciplines, such as machine learning or pattern recognition. However, most of the research has been addressed towards supervised tasks, paying little attention to unsupervised learning. In this paper, we introduce an unsupervised feature selection method for symbolic clustering tasks. Our method is based upon the assumption that, in the absence of class labels, we can deem as irrelevant those features that exhibit low dependencies with the rest of features. Experiments with several data sets demonstrate that the proposed approach is able to detect completely irrelevant features and that, additionally, it removes other features without significantly hurting the performance of the clustering algorithm.

Queries on probabilistic databases would be based on approximate matching rather than exact matching. This is partly due to the fact that the user may not know what are the exact probabilities of objects in a database. On the other hand, the domain of the attribute of a 1NF relational scheme is generally required finite. But the domain (0, 1] of the attribute that describes the probabilistic significance of an object is infinite. This means that it does not seem appropriate for approximate queries. In order to perform anything useful, a probabilistic data model is advocated for representing probabilistic data in this paper. The model is based on our definition of the nearest neighbor of data, which is used to measure the equality of probabilistic data. As a result, the approximation and infinite semantics of probabilistic data can be modeled in the nearest neighbor. Furthermore, a probabilistic relational algebra is also proposed so as to approximately query such databases.

Most fuzzy controllers and fuzzy expert systems must predefine membership functions and fuzzy inference rules to map numeric data into fuzzy linguistic values and to make fuzzy reasoning work. Recently, fuzzy systems that automatically derive fuzzy if-then rules from numeric data have been developed. In this paper, we propose a new learning method to automatically derive membership functions and fuzzy if-then rules from a set of given training examples. This method adopts a different way in building initial membership functions, thus making the learning procedure simpler than that used in [10]. Experiments are also made to show the performance of the newly proposed learning algorithm.

This paper describes a new approach to indexing time series. Indexing time series is more problematic than indexing text since in extreme we need to find all possible subsequences in the time series sequence. We propose to use signature files to index the time series and we also propose a new method to index the signature files to speed up the search of large time series. We propose a novel index structure, the signature tree, for time series indexing. We implemented the signature tree and we discuss its performance.