Earticle

This paper highlights Fuzzy Information retrieval approach for Biological databases where the lower level gene differentiation is very complex. We suggest a methodology FLi-BSML using the concept of possibilitic Ontology and Fuzzy Linguistic variables.

This paper focuses on generalization of rough set model and rule induction. First a extension of rough set approximations is established on general granular structure, so that the rough set models on some special granular structures are meaningful. The new rough approximation operators are interpreted by topological terminology well. Conversely, by means of the new rough approximation operators, many special granular structures, such as, covering, knowledge space, topology space and Pawlak approximation space, are characterized. Furthermore, using new approximation operators, two types of decision rules can be induced.

The reduced support vector machine (RSVM) is extension method of smooth support vector machine (SSVM) for handling computational difficulties as well as reduces the model complexity by generating a nonlinear separating surface for a large dataset. To generate representative reduce set for RSVM, clustering reduced support vector machine (CRSVM) was proposed. However, CRSVM is restricted to solve classification problems for large dataset with numeric attributes. In this paper, we propose an alternative algorithm, k-mode RSVM (KMO-RSVM) that combines RSVM and k-mode clustering technique to handle classification problems on categorical large dataset. Applying k-mode clustering algorithm to each class, we can generate cluster centroids of each class and use them to form the reduced set which is used in RSVM. In our experiments, we tested the effectiveness of KMO-RSVM on four public available dataset. It turns out that KMO-RSVM can improve speed of running time significantly than SSVM and still obtained a high accuracy. Comparison with RSVM indicates that KMO-RSVM is faster, gets smaller reduced set and comparable testing accuracy than RSVM.

Summaries or abstracts available with medical articles are useful for the physicians, medical students and patients to know rapidly what is the article about and decide whether articles are suitable for in-depth study. Since all medical text documents do not come with author written abstracts or summaries, an automatic medical text summarization system can facilitate rapid medical information access on the web. We approach the problem of automatically generating summary from medical article as a supervised learning task. We treat a document as a set of sentences, which the learning algorithm must learn to classify as positive or negative examples of sentences based on summary worthiness of the sentences. We apply the machine learning algorithm called bagging to this learning task, where a C4.5 decision tree has been chosen as the base learner. We also compare the proposed approach to some existing summarization approaches.