i n troduction: cancer is a major cause of mortality in the modern world, and one of the most important health problems in societies. during recent years, research on cancer as a system biology disease is focused on molecular differences between cancer cells and healthy cells. most of the proposed methods for classifying cancer using gene expression data act as black boxes and lack biological i...

We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike tr...

The definitions of the number of fuzzy sets and their proper distribution on their domains are fundamental issues for fuzzy systems since these basic parameters deeply affect the quality of the systems results, both in terms of performance rates and interpretability. Several methods have been proposed in the literature to define these parameters, although it is common to find works in which the...

This 1)aper focuses on the issue of named entity chunking in Japanese named entity recognition. We apply the SUl)ervised decision list lean> ing method to Japanese named entity recognition. We also investigate and in(:ori)orate several named-entity noun phrase chunking tech.niques and experimentally evaluate and con> t)are their l)erfornlanee, ill addition, we t)rot)ose a method for incorporati...

Pattern recognition is a collection of computer techniques to classify various observations into different clusters of similar attributes in either supervised or unsupervised manner. Application of fuzzy logic to unsupervised classification or clustering methods has resulted in many wildly used techniques such as fuzzy c-means (FCM) method. However, when the observations are too noisy, the perf...

Approaches to named entity recognition that rely on hand-crafted rules and/or supervised learning techniques have limitations in terms of their portability into new domains as well as in the robustness over time. For the purpose of overcoming those limitations, this paper evaluates named entity chunking and classi cation techniques in Japanese named entity recognition in the context of minimall...

Hard c-means can be used for building classifiers in supervised machine learning. For example, in a n-class problem, c clusters are built for each of the classes. This results into n · c centroids. Then, new examples can be classified according to the nearest centroid. In this work we consider the problem of building classifiers using fuzzy clustering techniques. In particular, we consider the ...

Fuzzy Decision Tree (FDT) classifiers combine decision trees with approximate reasoning offered by fuzzy representation to deal with language and measurement uncertainties. When a FDT induction algorithm utilizes stopping criteria for early stopping of the tree's growth, threshold values of stopping criteria will control the number of nodes. Finding a proper threshold value for a stopping crite...

This paper focuses on the change of named entities over time and its influence on the performance of the named entity tagger. First, we analyze Japanese named entities which appear in Mainichi Newspaper articles published in 1995, 1996, 1997, 1998 and 2005. This analysis reveals that the number of named entity types and the number of named entity tokens are almost steady over time and that 70 ∼...