this paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein superfamily. since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. these features are the occurrence probabilities of six exchange groups in the seque...

The main objective of this research work is to construct a Fuzzy Temporal Rule Based Classifier that uses fuzzy rough set and temporal logic in order to mine temporal patterns in medical databases. The lower approximation concepts and fuzzy decision table with the fuzzy features are used to obtain fuzzy decision classes for building the classifier. The goals are pre-processing for feature selec...

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capa...

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...

When individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision trees, i.e., a Fuzzy Random Forest, is proposed. This ...

The Vapnik-Chervonenkis (VC) dimension measures the complexity of a learning machine, and a low VC dimension leads to good generalization. The recently proposed Minimal Complexity Machine (MCM) learns a hyperplane classifier by minimizing an exact bound on the VC dimension. This paper extends the MCM classifier to the fuzzy domain. The use of a fuzzy membership is known to reduce the effect of ...

In a vector quantisation (VQ) based speaker identification system, a speaker model is created for each speaker from the training speech data by using the k-means clustering algorithm. For an unknown utterance analysed into a sequence of vectors, the nearest prototype classifier is used to identify speaker. To achieve the higher speaker identification accuracy, a fuzzy approach is proposed in th...

This paper proposes a fuzzy methodology to translate the natural language descriptions of the TW3 method for bone age assessment into an automatic classifier. The classifier is built upon a modified version of a fuzzy ID3 decision tree. No large data records are needed to train the classifier, i.e., to find out the classification rules, since the classifier is built upon rules given by the TW3 ...

This paper proposes a fuzzy classifier based on type-2 fuzzy sets to be applied in land cover classification. The classifier is built on the basis of the available data and considers the merging of information drawn from different experts. The data regard a thematic mapper representing the land cover of a real plain cultivated area. The experts are represented by different bands which classify ...

Development of fuzzy ifthen rules and formation of membership functions are the important consideration in designing a fuzzy classifier system. This paper presents a Modified Genetic Algorithm (ModGA) approach to obtain the optimal rule set and the membership function for a fuzzy classifier. In the genetic population, the membership functions are represented using real numbers and the rule set ...