Intelligent Data Analysis: An International Journal

Feature selection has been the focus of interest for quite some time and much work has been done. With the creation of huge databases and the consequent requirements for good machine learning techniques, new problems arise and novel approaches to feature selection are in demand. This survey is a comprehensive overview of many existing methods from the 1970's to the present. It identifies four steps of a typical feature selection method, and categorizes the different existing methods in terms of generation procedures and evaluation functions, and reveals hitherto unattempted combinations of generation procedures and evaluation functions. Representative methods are chosen from each category for detailed explanation and discussion via example. Benchmark datasets with different characteristics are used for comparative study. The strengths and weaknesses of different methods are explained. Guidelines for applying feature selection methods are given based on data types and domain characteristics. This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications.

This article addresses the problem of analyzing existing discretizations of continuous attributes with regard to their redundancy and minimality properties. The research was inspired by the increasing number of heuristic algorithms created for generating the discretizations using various methodologies, and apparent lack of any direct techniques for examining the solutions obtained as far as their basic properties, (e.g., the redundancy), are concerned. The proposed method of analysis fills this gap by providing a test for redundancy and enabling for a controlled reduction of the discretization's size within specified limits. Rough set theory techniques are used as the basic tools in this method. Exemplary results of discretization analyses for some known real-life data sets are presented for illustration.

In this article, we first explore an intrinsic problem that exists in the models induced by learning algorithms. Regardless of the selected algorithm, search methodology and hypothesis representation by which the model is induced, one would expect the model to make better predictions in some regions of the description space than others. We present the fact that an induced model will have some regions of relatively poor performance: the problem of locally low predictive accuracy. Holte, Arker, Porter [21] addressed this intrinsic problem in learning systems that describe the induced model as a disjunction of conjunctions of conditions. In this article, we investigate the characterisation of the problem in instance-based and Naive Bayesian classifiers.

Having characterised the problem of locally low predictive accuracy, we propose to counter the problem in these two types of learning algorithms, using a composite learner framework. The strategy is to select an estimated better performing model to do the final prediction during classification. Empirical results from fifteen real-world domains show that the strategy is capable of partially overcoming the problem of locally low predictive accuracy, and at the same time improving the overall performance of its constituent algorithms in most of the domains studied. The composite learner is also found to outperform four methods of stacked generalisation, and also a model selection method based on cross-validation, in most of the experimental domains studied.

This article addresses the issue of quantitative information measurement within the Dempster–Shafer belief function formalism. Entropy computation in Dempster–Shafer depends on the way uncertainty measures are conceptualized. However, freed of most probability constraints, uncertainty measures in Dempster–Shafer theory can lead to further advances in optimization in information theory, which in turn may have a wide impact on decision and control. This article examines one form of current development regarding the entropy measure induced from the measure of dissonance. For a significant period, the measure of dissonance has been taken as a measure of entropy. We present in this article the entropy measure as a monotonically decreasing function, symmetrical to the measure of dissonance.