Recognizing Imbalanced Classes by an Intuitionistic Fuzzy Classifier

2015 ◽  
pp. 233-247
Author(s):  
Eulalia Szmidt ◽  
Janusz Kacprzyk ◽  
Marta Kukier
Keyword(s):  
Fuzzy Classifier ◽  
Intuitionistic Fuzzy ◽  
Imbalanced Classes

A New Approach to Classification of Imbalanced Classes via Atanassov's Intuitionistic Fuzzy Sets

2009 ◽  
pp. 85-101 ◽  
Author(s):  
Eulalia Szmidt ◽  
Marta Kukier
Keyword(s):  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Fuzzy Classifier ◽  
Frequency Distributions ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Classifiers ◽  
Imbalanced Classes ◽  
Confusion Matrices ◽  
Atanassov’S Intuitionistic Fuzzy Sets

We present a new method of classification of imbalanced classes. The crucial point of the method lies in applying Atanassov’s intuitionistic fuzzy sets (which are a generalization of fuzzy sets) while representing the classes during the first training phase. The Atanassov’s intuitionistic fuzzy sets are generated according to an automatic and mathematically justified procedure from the relative frequency distributions representing the data. Next, we use the information about so-called hesitation margins (which besides membership and non-membership values characterize Atanassov’s intuitionistic fuzzy sets) making it possible to improve the results of data classification. The results obtained in the testing phase were examined not only in the sense of general error/accuracy but also by using confusion matrices, that is, exploring a detailed behavior of the intuitionistic fuzzy classifiers. Detailed analysis of the errors for the examined examples has shown that applying Atanassov’s intuitionistic fuzzy sets gives better results than the counterpart approach via fuzzy sets. Better performance of the intuitionistic fuzzy classifier concerns mainly the recognition power of a smaller class. The method was tested using a benchmark problem from UCI machine learning repository.

scholarly journals Intelligent Intuitionistic Fuzzy with Elephant Swarm Behaviour Based Rule Pruning for Early Detection of Alzheimer in Heterogeneous Multidomain Datasets

2019 ◽  
Vol 8 (4) ◽  
pp. 9291-9298
Keyword(s):  
Smart Home ◽  
Information Technologies ◽  
Prediction Models ◽  
Intelligent System ◽  
Early Stage ◽  
Elderly Person ◽  
Misclassification Rate ◽  
Elderly Persons ◽  
Fuzzy Classifier ◽  
Intuitionistic Fuzzy

In this Digital age, a rapid advancement in rise of both communication and information technologies provide many services by incorporating intelligent system of automated health assessment of resident welfare. This is achieved by tracking elderly persons activities using smart home technologies and with this activity-based learning, it helps to discover individuals suffering from early stage of Alzheimer can be predicted without distributing their living style. The main purpose of this paper is to use two different domains of datasets for predicting the alzheimer’s in elderly person during its initial stage. Thus, this work uses ubiquitous computing technologies like smart home dataset which collects the daily activities of individuals and as the clinical dataset for prediction of alzheimer’s. The objective of this proposed work is to handle the hesitancy of uncertainty by introducing intelligent intuitionistic fuzzy classifier, which inhibits irrelevant rule generation by acquiring the knowledge of elephant swarm behavior (IIF-ESB). Using elephant swarm search behavior, the rules generated by intuitionistic fuzzy are finetuned to ovoid overfitting problem and thus it eliminates the irrelevant rules. The selected potential rules highly influence the accuracy rate of the prediction model in presence of uncertainty. Performance result of the proposed model (IIF-ESB) proved that with the ability to handle the impreciseness in prediction of alzheimer’s, the usage of degree of hesitancy and intelligent of elephant swarm searching behaviour increases the accurate prediction rate and decrease the misclassification rate considerably while compared with existing prediction models.

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

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