Acquiring precise data is expensive. Hence, there is interest in the development of computer algorithms that make full use of imprecise data, even though these new algorithms may be more complex and require more resources. In particular, most Genetic Fuzzy Systems (GFS). accept crisp inputs, but changes can be effected to them so that rules are obtained from vague data. Furthermore, the rule ge...

This paper presents a system for classifying e-mails into two categories, legitimate and fraudulent. This classifier system is based on the serial application of three filters: a Bayesian filter that classifies the textual content of e-mails, a rulebased filter that classifies the non grammatical content of e-mails and, finally, a filter based on an emulator of fictitious accesses which classif...

We present two methods for entity linking in two of our systems submitted to TAC-KBP 2012. The first one, implemented in JVNTDT1 system, learns coherence among cooccurrence entities referred to within a text by exploiting Wikipedia’s link structure and the second one, implemented in JVN_TDT2 system, combines some heuristics with a statistical model, for entity linking. The method implemented in...

In this paper we examine the performance of fuzzy rule-based systems with classification priority for medical diagnosis problems. The assumption in this paper is that a classification priority is given a priori for each class in a pattern classification problem. Our fuzzy rulebased system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patt...

Belief updating schemes in artificial intelligence may be viewed as three dimensional languages, consisting of a syntax (e.g. probabilities or certainty factors), a calculus (e.g. Bayesian or CF combination rules), and a semantics (i.e. cognitive interpretations of competing formalisms). This paper studies the rational scope of those languages on the syntax and calculus grounds. In particular, ...

An effective automated pulmonary nodule detection system can assist radiologists in detecting lung abnormalities at an early stage. In this paper, we propose a novel pulmonary nodule detection system based on a genetic programming (GP)-based classifier. The proposed system consists of three steps. In the first step, the lung volume is segmented using thresholding and 3D-connected component labe...

In this paper we propose a new type of distance-based classifier. Traditionally, these classifiers are instancebased: they classify a test instance by computation of a similarity measure between that instance and the instances in the training-set and assigning it the same class of the most similar k instances. This method is simple but has some disadvantages, among which there is the greater se...