Data Reduction with Rough Sets

Data Reduction with Rough Sets

Richard Jensen (Aberystwyth University, UK)
Copyright: © 2009 |Pages: 5
DOI: 10.4018/978-1-60566-010-3.ch087
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Abstract

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques fall in to one of the following categories: those that transform the underlying meaning of the data features and those that are semantics-preserving. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation function. The process aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In knowledge discovery, feature selection methods are particularly desirable as they facilitate the interpretability of the resulting knowledge. For this, rough set theory has been successfully used as a tool that enables the discovery of data dependencies and the reduction of the number of features contained in a dataset using the data alone, while requiring no additional information.
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Background

The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In many real world problems FS is a must due to the abundance of noisy, irrelevant or misleading features. A detailed review of feature selection techniques devised for classification tasks can be found in (Dash & Liu, 1997).

The usefulness of a feature or feature subset is determined by both its relevancy and its redundancy. A feature is said to be relevant if it is predictive of the decision feature(s) (i.e. dependent variable(s)), otherwise it is irrelevant. A feature is considered to be redundant if it is highly correlated with other features. Hence, the search for a good feature subset involves finding those features that are highly correlated with the decision feature(s), but are uncorrelated with each other.

A taxonomy of feature selection approaches can be seen in Figure 1. Given a feature set of size n, the task of any FS method can be seen as a search for an ‘’optimal’’ feature subset through the competing 2n candidate subsets. The definition of what an optimal subset is may vary depending on the problem to be solved. Although an exhaustive method may be used for this purpose in theory, this is quite impractical for most datasets. Usually FS algorithms involve heuristic or random search strategies in an attempt to avoid this prohibitive complexity.

Figure 1.

Feature selection taxonomy

Determining subset optimality is a challenging problem. There is always a trade-off in non-exhaustive techniques between subset minimality and subset suitability - the task is to decide which of these must suffer in order to benefit the other. For some domains (particularly where it is costly or impractical to monitor many features, such as complex systems monitoring (Shen & Jensen, 2004)), it is much more desirable to have a smaller, less accurate feature subset. In other areas it may be the case that the modeling accuracy (e.g. the classification rate) using the selected features must be extremely high, at the expense of a non-minimal set of features, such as web content categorization (Jensen & Shen, 2004b).

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