This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility gra...

Tree-to-tree translation model is widely studied in statistical machine translation (SMT) and is believed to be much potential to achieve promising translation quality. However, the existing models still suffer from the unsatisfactory performance due to the limitations both in rule extraction and decoding procedure. According to our analysis and experiments, we have found that tree-to-tree mode...

Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered by sparse rule bases. In most engineering applications, the use of more than one input variable is common. This...

Fuzzy controllers with variable universe of discourse (VUD) have been applied in many fields of intelligent controlling because of their high-accuracy performance. This paper provides a lookup table method to design backing-up fuzzy controllers based on VUD. By setting a set of random start points, input–output data pairs are obtained using test-driving method. One data pair defines one fuzzy r...

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (...

A prototype system has been designed to automate the extraction of bibliographic data (e.g., article title, authors, abstract, affiliation and others) from online biomedical journals to populate the National Library of Medicine’s MEDLINE® database. This paper describes a key module in this system: the labeling module that employs statistics and fuzzy rule-based algorithms to identify segmented ...

An automated labeling (AL) module has been developed to automate the extraction of bibliographic data (e.g., article title, authors, affiliation, abstract, and others) from online biomedical journals for the National Library of Medicine’s MEDLINE database. The AL module employs string matching, statistics, and fuzzy rule-based algorithms to identify segmented zones in an article’s HTML pages a...

The “Double Fuzzy Point” rule representation opens a new dimension for expressing changes of fuzziness in fuzzy rule-based systems. In the case of standard “Fuzzy Point” rule representations, it is difficult to describe fuzzy functions in which crisp observations are required to have fuzzy conclusions, or in which an increase in the fuzziness of observations leads to reduced fuzziness in conclu...

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training in...