Type-2 Fuzzy Classifier Research Articles

A genetic interval type-2 fuzzy logic-based approach for generating interpretable linguistic models for the brain P300 phenomena recorded via brain–computer interfaces

One of the important areas of brain---computer interface (BCI) research is to identify event-related potentials (ERPs) which are spatial---temporal patterns of the brain activity that happen after presentation of a stimulus and before execution of a movement. One of the important ERPs is the P300 which is an endogenous component of ERPs with a latency of about 300 ms which is elicited by significant stimuli (visual, or auditory). Various machine learning-based classifiers have been used to predict the P300 events and relate them to the human intended activities. However, the vast majority of the employed techniques like Bayesian linear discriminant analysis (BLDA) and regularized fisher linear discriminant analysis (RFLDA) are black box models which are difficult to understand and analyse by a normal clinician. In addition, due to the inter- and intra-user uncertainties associated with the P300 events, most of the existing classifiers need to be trained for a specific user under specific circumstances and the classifier needs to be retrained for different users or change of circumstances. In this paper, we present an interval type-2 fuzzy logic-based classifier which is able to handle the users' uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximise the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. We will present various experiments which were performed on standard data sets and using real-data sets obtained from real subjects' experiments performed in the BCI laboratory in King Abdulaziz University. It will be shown that the produced type-2 fuzzy logic-based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets.

Emitter identification of electronic intelligence system using type-2 fuzzy classifier

Emitter signal recognition is one of the key procedures in signal processing of electronic intelligence (ELINT). In particular, the identification of radar emitters has been important with the advances in radio frequency, electronics and control technologies. Jitter is an unintentional form of modulation that can have a wide variety of sources. Timing-related data errors will occur if jitter is beyond acceptable limits. Designers need a fast and easy way to obtain a complete characterization of clock jitter in the microprocessor controlled. To enhance the ability of specific emitter identification (SEI) to meet the requirement of modern ELINT, a novel identification approach for radar emitter signals based on type-2 fuzzy classifier is presented in this paper. In fuzzy type-2 sets, the uncertainty is represented as an extra dimension. In this article, we show how it is possible to reduce the effect of SEI-induced highly jittered radar emitters in ELINT, with the classifiers of type-1 and type-2 fuzzy logic. This work discusses the impact of unknown jitter sampling on signal estimation. Based on the ELINT feature extraction of radar emitter signals, the type-2 fuzzy classifier is applied to identification of radar emitters effectively. Experiment results shows that the approach can achieve high accurate classification even at higher error deviation level and has good characteristics of identification.

Land Cover Classification Based on General Type-2 Fuzzy Classifiers

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 the spectral sensor information. The new proposed method to design the classifier as well as the use of general type-2 fuzzy sets allows the modeling of input-output relations and minimizes the effects of uncertainties in the usual fuzzy rule-based classifiers. The experiments carried out attest to the efficiency of the proposed general type-2 fuzzy classifier.

Type-2 fuzzy logic-based classifier fusion for support vector machines

As a machine-learning tool, support vector machines (SVMs) have been gaining popularity due to their promising performance. However, the generalization abilities of SVMs often rely on whether the selected kernel functions are suitable for real classification data. To lessen the sensitivity of different kernels in SVMs classification and improve SVMs generalization ability, this paper proposes a fuzzy fusion model to combine multiple SVMs classifiers. To better handle uncertainties existing in real classification data and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS), we apply interval type-2 fuzzy sets to construct a type-2 SVMs fusion FLS. This type-2 fusion architecture takes considerations of the classification results from individual SVMs classifiers and generates the combined classification decision as the output. Besides the distances of data examples to SVMs hyperplanes, the type-2 fuzzy SVMs fusion system also considers the accuracy information of individual SVMs. Our experiments show that the type-2 based SVM fusion classifiers outperform individual SVM classifiers in most cases. The experiments also show that the type-2 fuzzy logic-based SVMs fusion model is better than the type-1 based SVM fusion model in general.

MPEG VBR video traffic modeling and classification using fuzzy technique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters.