Traditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the ‘standard’ Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate propertie...

This paper proposes a semi-supervised learning method using Fuzzy clustering to solve word sense disambiguation problems. Furthermore, we reduce side effects of semi-supervised learning by ensemble learning. We set classes for labeled instances. The -th labeled instance is used as the prototype of the -th class. By using Fuzzy clustering for unlabeled instances, prototypes are moved to more sui...

In this paper a new technique named as Intuitionistic fuzzy max-min average composition method is proposed to construct the decision method for Medical Diagnosis using different types of Intuitionistic fuzzy soft matrices and its operations. Sanchez’s approach for decision making is studied and the concept is generalized by the application of Intuitionistic fuzzy soft set theory. Through a surv...

Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be e...

To find an optimal path for robots in an environment that is only partially known and continuously changing is a difficult problem. This paper presents a new method for generating a collision-free near-optimal path for an autonomous mobile robot in a dynamic environment containing moving and static obstacles using neural network and fuzzy logic with genetic algorithm. The mobile robot selects a...

Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks natural language processing. However, despite widespread use NER models, they still require a large-scale labeled data set, which incurs heavy burden due to manual annotation. Domain adaptation promising solutions this problem, where rich from relevant source domai...

The fuzzy c-means clustering algorithm (and a supervised classiier based on it) requires the a priori selection of a weighting parameter called the fuzzy exponent (denoted m). Guidance in the existing literature on an appropriate value of m is not deenitive. This paper determines suitable values of m by using the criterion that fuzzy set memberships reeect class proportions in the mixed pixels ...

The discovery of interesting regions in spatial datasets is an important data mining task. In particular, we are interested in identifying disjoint, contiguous regions that are unusual with respect to the distribution of a given class; i.e. a region that contains an unusually low or high number of instances of a particular class. This paper centers on the discussion of techniques, methodologies...