This paper aims to present an improved rule-based fingerprint classification using coherence method and a fuzzy rule-based (or simply fuzzy-based) fingerprint classification. The improved classification using coherence method has been applied to fingerprint images that do not previously produce any singular points while the fuzzy rule-based classification is designed based on the uncertainty in...

In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rulebased classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge ...

In this paper we first introduce the concept of classification confidence in fuzzy rule-based classification. Classification confidence shows the strength of classification for an unseen pattern. Low classification confidence for an unseen pattern means that the classification of that pattern is not very clear compared to that with high classification confidence. Then we focus on the minimum cl...

Analogical proportion-based classification methods have been introduced a few years ago. They look in the training set for suitable triples of examples that are in an analogical proportion with the item to be classified, on a maximal set of attributes. This can be viewed as a lazy classification technique since, like k-nn algorithms, there is no static model built from the set of examples. The ...