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The question how to manage the contradictive requirements of accuracy and compactness in classification systems remains an important question in machine learning and data mining. This paper proposes a approach that belongs to the domain of fuzzy rule-based classification and uses the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative...
According to the World Health Organization, breast cancer is the most common type of cancer in women. It is also the second leading cause of death among women around the world, becoming the most fatal form of cancer. However, to detect and classify masses is a hard task even for experts. Therefore, due to medical experience, different diagnoses to an image are commonly found. The use of a computer...
Humans have the natural capabilities to perceive and anticipate actions of objects they interact with, including incidents happen within their neighborhood. These days, this important aspect of human perception has been widely incorporated in the computer vision framework to perform human action detection task. However, little attention is paid to the problem of detecting ongoing human actions as...
We introduce a novel adaptive neuro-fuzzy architecture based on the framework of Multiple Instance Fuzzy Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS) [1] that is designed to handle reasoning with multiple instances (bags of instances) as input and capable of learning from ambiguously labeled...
Fuzzy Rule based regression, classification and control have found great use in modern applications due to its simplicity, flexibility and capability. A key issue in all such methods is the computation time. Computational complexity of training and testing is linearly dependent on the size of fuzzy rule base and the respective fuzzy rule space is exponentially dependent on data dimensionality. Sparse...
In this paper, we assume that we have two types of datasets for classifier design. One is an in-house dataset which is fully available for classifier design as training data. The other is an external dataset which is kept under a very severe privacy preserving policy. We assume that the available information on the external dataset is only the error rate of a presented classifier. No other information...
In this paper, an alternative stability analysis for the Levenberg-Marquardt (LM) algorithm is proposed for the training of Type-2 fuzzy neural networks (T2FNNs). The benefit of the proposed stability analysis is that it does not require any eigenvalues computations, and hence it is simpler when compared to the existing stability analysis studies in literature.
In this paper, we propose a fast and economic strategy for the integration of new classes on the fly into evolving fuzzy classifiers (EFC) during data stream mining processes. Fastness addresses the assurance that a newly arising class in the stream can be integrated in a way such that the classifier is able to correctly return the new class after receiving only a few training samples of it. Economic...
The Common Spatial Pattern (CSP) is an effective algorithm used in EEG based Brain Computer Interface (BCI) to extract discriminative features, however, its effectiveness depends upon the subject-specific frequency bands. Also, the generated features using CSP are non-stationary in nature. In this paper, we propose a Meta-cognitive Interval type-2 Neuro-Fuzzy Inference System to handle non-stationarity...
This paper introduces two fuzzy fingerprint based text classification techniques that were successfully applied to automatically label companies from CrunchBase, based purely on their unstructured textual description. This is a real and very challenging problem due to the large set of possible labels (more than 40) and also to the fact that the textual descriptions do not have to abide by any criteria...
In multiple instance learning (MIL) setting, instances are grouped together in different labeled bags and the classifier tries to learn the label of unknown bags or instances. This is significantly different from traditional supervised learning techniques where the instances are labeled itself. In this work, a fuzzy based citation-kNN technique, which uses modified Hausdorff distance between bags,...
This paper introduces a preliminary extension of the Fuzzy Cognitive Map (FCM) architecture based on Lattice Computing (LC) techniques namely Linguistic Fuzzy Cognitive Maps (LFCM). The proposed LFCM is able to handle large scale data in pattern classification applications. This enhancement is achieved by applying a novel data meta-representation, defined in a mathematical lattice, including several...
In this paper we investigate the use of fuzzy rule-based classifiers for multi-label classification. This classification task deals with problems where more than one label could be assigned simultaneously to a given instance. We concentrate on problem transformation methods, which use different strategies to transform a multi-label problem into a different single-label classification problems. This...
Random forests have proved to be very effective classifiers, which can achieve very high accuracies. Although a number of papers have discussed the use of fuzzy sets for coping with uncertain data in decision tree learning, fuzzy random forests have not been particularly investigated in the fuzzy community. In this paper, we first propose a simple method for generating fuzzy decision trees by creating...
The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm...
In many real problems the regression models have to be accurate but, also, interpretable in order to provide qualitative understanding of the system. In this realm, the use of fuzzy rule base systems, particularly TSK, is widely extended. TSK rules combine the interpretability and expressiveness of rules with the ability of fuzzy logic for representing uncertainty, and the precision of the polynomials...
The use of fuzzy quantifiers to modify the fuzzy linguistic terms in fuzzy models helps build fuzzy systems in a more natural way, by capturing finer pieces of information embedded in the training data. This paper presents a practical approach for the acquisition of fuzzy production rules with quantifiers, based on a class-dependent simultaneous rule learning strategy where each class is associated...
This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper...
Generalized conjunction/disjunction (GCD) is a fundamental idempotent logic function that enables a continuous transition from conjunction to disjunction. GCD is a building block for all idempotent compound aggregation structures. In this paper we investigate various implementations of GCD. Our goals are to provide justifiable andness/orness rating scales, to propose GCD implementations that have...
The advantages of multi-classification schemes based on decomposition strategies, and especially the One-vs-One framework, have been stressed even for those algorithms that can address multiple classes. However, there is an inherent hitch for the One-vs-One learning scheme related to the decision process: the non-competent classifier problem. This issue refers to the case where a binary classifier...
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