Covering rough set-based incremental feature selection for mixed decision system

Incremental Feature Selection Based on Rough Set Theory (PhD Proposal)

Audio Feature Selection Based on Rough Set

Keeping audio features is important for audio index. However, in most cases the features number is huge, thus direct processing is time-consuming. Feature selection, as a preprocessing step of data mining, has turned to be very efficient in reducing dimensionality and removing irrelevant data. In this paper, we propose a feature selection algorithm based on Rough Set theory, which could find ou...

Rough Set Extensions for Feature Selection

Rough set theory (RST) was proposed as a mathematical tool to deal with the analysis of imprecise, uncertain or incomplete information or knowledge. It is of fundamental importance to artificial intelligence particularly in the areas of knowledge discovery, machine learning, decision support systems, and inductive reasoning. At the heart of RST is the idea of only employing the information cont...

Rough Set Feature Selection Algorithms for Textual Case-Based Classification

Feature selection algorithms can reduce the high dimensionality of textual cases and increase case-based task performance. However, conventional algorithms (e.g., information gain) are computationally expensive. We previously showed that, on one dataset, a rough set feature selection algorithm can reduce computational complexity without sacrificing task performance. Here we test the generality ...

Clustering Using Rough - Set Feature Selection

`Feature selection aims to remove features unnecessary to the target concept. Rough-set theory (RST) eliminates unimportant or irrelevant features, thus generating a smaller (than the original) set of attributes with the same, or close to, classificatory power. Clustering, also a form of data grouping, groups a set of data such that intra-cluster similarity is maximized and inter-cluster simila...