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...

This paper presents an intelligent diagnostic supporting system – iDiaKAW (Intelligent and Interactive Knowledge Acquisition Workbench), which automatically extracts useful knowledge from massive medical data to support real medical diagnosis. In which, our two novel pre-processing algorithms MIDCA (Multivariate Interdependent Discretization for Continuousvalued Attributes) and LUIFS (Latent Ut...

We describe a gold standard for semantic verb classes which is based on human associations to verbs. The associations were collected in a web experiment and then applied as verb features in a hierarchical cluster analysis. We claim that the resulting classes represent a theory-independent gold standard classification which covers a variety of semantic verb relations, and whose features can be u...

Feature selection (FS) refers to the problem of selecting those input attributes that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. This has found application in tasks th...

This paper presents a fully scalable image coding scheme based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The proposed algorithm, called Fully Scalable SPIHT (FS-SPIHT), adds the spatial scalability feature to the SPIHT algorithm. It provides this new functionality without sacrificing other important features of the original SPIHT bitstream such as: compression efficiency,...

A topic detection model based on hierarchical clustering for Chinese microblog is proposed in this paper. In order to minimize the impact of noise, we optimize the feature selection and weight computation method and use a new scoring method to filter out those topic-unrelated tweets. We also give an improved topic detection algorithm which uses a new vector distance calculation method and cente...

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known “curse of dimensionality”. As a consequence, in order to extract useful information from...

Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In field of gene analysis, several matrix factorization-based dimensionality reduction methods been developed. However, such can still be improved terms efficiency reliability. this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised...

Land cover mapping (LCM) in complex surface-mined and agricultural landscapes could contribute greatly to regulating mine exploitation and protecting mine geo-environments. However, there are some special and spectrally similar land covers in these landscapes which increase the difficulty in LCM when employing high spatial resolution images. There is currently no research on these mixed complex...

Although feature selection is a central problem in inductive learning as suggested by the growing amount of research in this area, most of the work has been carried out under the supervised learning paradigm, paying little attention to unsupervised learning tasks and, particularly, clustering tasks. In this paper, we analyze the particular beneets that feature selection may provide in hierarchi...