http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

Neighborhood rough set based heterogeneous feature subset selection

Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition, and signal processing. This has found successful application in tasks that involve data sets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and Web content classification. One of the many successful applications of rough set theory has been to this feature selection area. This paper reviews those techniques that preserve the underlying semantics of the data, using crisp and fuzzy rough set-based methodologies. Several approaches to feature selection based on rough set theory are experimentally compared. Additionally, a new area in feature selection, feature grouping, is highlighted and a rough set-based feature grouping technique is detailed.

/pdf/semantics-preserving-dimensionality-reduction-rough-and-2t8flljhfa.pdf

Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches

Probabilistic rough set approximations

https://fada.birzeit.edu/bitstream/20.500.11889/5313/1/Whale%20optimization%20approaches%20for%20wrapper%20feature%20selection.pdf

Whale optimization approaches for wrapper feature selection

Practical machine learning algorithms are known to degrade in performance (prediction accuracy) when faced with many features (sometimes attribute is used instead of feature) that are not necessary for rule discovery. To cope with this problem, many methods for selecting a subset of features have been proposed. Among such methods, the filter approach that selects a feature subset using a preprocessing step, and the wrapper approach that selects an optimal feature subset from the space of possible subsets of features using the induction algorithm itself as a part of the evaluation function, are two typical ones. Although the filter approach is a faster one, it has some blindness and the performance of induction is not considered. On the other hand, the optimal feature subsets can be obtained by using the wrapper approach, but it is not easy to use because of the complexity of time and space. In this paper, we propose an algorithm which is using rough set theory with greedy heuristics for feature selection. Selecting features is similar to the filter approach, but the evaluation criterion is related to the performance of induction. That is, we select the features that do not damage the performance of induction.

Using Rough Sets with Heuristics for Feature Selection

Practical machine learning algorithms are known to degrade in performance when faced with many features that are not necessary for rule discovery. To cope with this problem, many methods for selecting a subset of features with similar-enough behaviors to merit focused analysis have been proposed. In such methods, the filter approach that selects a feature subset using a preprocessing step, and the wrapper approach that selects an optimal feature subset from the space of possible subsets of features using the induction algorithm itself as a part of the evaluation function, are two typical ones. Although the filter approach is a faster one, it has some blindness and the performance of induction is not considered. On the other hand, the optimal feature subsets can be obtained by using the wrapper approach, but it is not easy to use because the complexity of time and space. In this paper, we propose an algorithm of using the rough set methodology with greedy heuristics for feature selection. In our approach, selecting features is similar as the filter approach, but the performance of induction is considered in the evaluation criterion for feature selection. That is, we select the features that damage the performance of induction as little as possible.

Introduces an incremental, probabilistic rough set approach to rule discovery in very large, complex databases with uncertainty and incompleteness. The approach is based on the combination of generalization distribution table (GDT) and rough set methodology. A GDT is a table in which the probabilistic relationships between concepts and instances over discrete domains are represented. By using a GDT as an hypothesis search space and combining the GDT with the rough set methodology, noises and unseen instances can be handled, biases can be flexibly selected, background knowledge can be used to constrain rule generation, and the rules with strengths can be effectively acquired from very large, complex databases in an incremental, bottom-up mode. We focus on basic concepts and an implementation of our methodology.

An incremental, probabilistic rough set approach to rule discovery

This paper introduces a new approach for mining if-then rules in databases with uncertainty and incompleteness. The approach is based on the combination of Generalization Distribution Table (GDT) and the Rough Set methodology. A GDT is a table in which the probabilistic relationships between concepts and instances over discrete domains are represented. By using a GDT as a hypothesis search space and combining the GDT with the rough set methodology, noises and unseen instances can be handled, biases can be flexibly selected, background knowledge can be used to constrain rule generation, and if-then rules with strengths can be effectively acquired from large, complex databases in an incremental, bottom-up mode. In this paper, we focus on basic concepts and an implementation of our methodology.

Data Mining: A Probabilistic Rough Set Approach

This paper extends the notion of information tables and concept hierarchies of equivalence relations to binary relations. So extended rough set theory and attribute oriented generalization techniques can be used to mining binary relations in data.

Juzhen Dong

Papers

Using Rough Sets with Heuristics for Feature Selection

Using Rough Sets with Heuristics for Feature Selection

An incremental, probabilistic rough set approach to rule discovery

Data Mining: A Probabilistic Rough Set Approach

Frameworks for Mining Binary Relations in Data