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Recognizing relatively smaller classes (called imbalanced classes) from data is an important task both from a theoretical and practical points of view. In many real world problems smaller classes are usually more interesting from the user point of view but they are more difficult to obtain by a classifier. This paper, which is a continuation of our previous works, discusses a classifier that is based...
In most real-world problems, we are dealing with large size datasets. Reducing the number of irrelevant/redundant features dramatically reduces the running time of a learning algorithm and leads to a more general concept. In this paper, realization of feature selection through NeuroEvolution of Augmenting Topologies (NEAT) [1] is investigated which aims to pick a subset of features that are relevant...
The objective of this work is to present an improved version of a method to learn fuzzy classification rules from data by means of a multi-objective evolutionary algorithm and the iterative approach. The work presented here derives from a preliminary version previously proposed by the authors. In the previous version, the trade-off between accuracy and interpretability during the rule generation process...
Dental classification for periapical radiograph based on multiple fuzzy attribute is proposed, where each tooth is analyzed based on multiple criteria such as area/perimeter ratio and width/height ratio. A classification method on special type of dental image called periapical radiograph is studied and classification is done without speculative classification (in case of ambiguous object), therefore...
We develop a method to construct a fuzzy rule-based classifier which makes use of the pattern trees and Axiomatic Fuzzy Set (AFS) theory. The AFS framework supports a way on how to convert the information present in databases into the membership functions and their fuzzy logic operations. A selection index used for quantifying the discriminatory capabilities of the fuzzy concept was proposed. Being...
Support vector machines (SVMs) are learning systems based on statistical learning theory that have been applied with excellent generalization performance to a variety of applications in classification and regression. However, as Artificial Neural Networks, SVM are black box models, that is, they do not explain the process by which a given result is attained. Some models that extract rules from trained...
In this study, we investigate the use of a Fuzzy C-Means Clustering based Neural Network (FNN) classifier in problems of emotion classification. The proposed classifier model consists of three layers, namely, input, hidden and output layers. Here, fuzzy c-means clustering method, two types of polynomial and linear combination function are used as a kernel function in the input layer, the hidden layer...
In this paper we present two modified and improved versions of the formerly published Fuzzy-Based Single-Stroke Character Recognizer (FUBAR) algorithm. After introducing the original method, the study investigates the effects of two different improvements of the designed algorithm. The first extension is the use of symbol-dependent fuzzy grids to extract symbol features; the second one is the use...
We provide a summary of the goals, underlying philosophy, work, decisions, product, and completion schedule of the IEEE P-1788 working group on interval arithmetic.
Fuzzy Associative Classification has attracted remarkable research attention for knowledge discovery and business analytics in recent years due to its merits in accuracy and linguistic modeling. Furthermore, it is deemed meaningful to construct an associative classifier with a compact set of rules (i.e., compactness), which is easy to understand and use in decision making. This paper introduces a...
The clustering of web search has become a very interesting research area among academic and scientific communities involved in information retrieval. Clustering of web search result systems, also called Web Clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for web document clustering already...
In many practical situations, we need, given the values of the observed quantities x1;…; xn, to predict the value of a desired quantity y. To estimate the accuracy of a prediction algorithm ƒ(x1; … xn), we need to compare the results of this algorithm's prediction with the actually observed values.
To preserve privacy, the original data points (with exact values) are replaced by boxes containing each (inaccessible) data point. This privacy-motivated uncertainty leads to uncertainty in the statistical characteristics computed based on this data. In a previous paper, we described how to minimize this uncertainty under the assumption that we use the same standard statistical estimates for the desired...
This paper advocates a novel approach to fuzzy systems modeling called fuzzy pattern trees. This approach is largely motivated by alleged disadvantages of rule-based system architectures that still dominate the field. Due to its hierarchical, modular structure and the use of different types of (nonlinear) aggregation operators, a fuzzy pattern tree has the ability to represent functional dependencies...
This article is focused on automatic recognition of jewelery stones quality. An image recognition method is described. Relevant image characteristics are computed, which are then used to classify the stone quality. Classification is performed by an algorithm based on binary decision trees with the decision thresholds adapted from a training dataset. At the end, the time complexity as well as accuracy...
Adolescent Idiopathic Scoliosis is a spinal deformity sometimes treated using a brace. Brace treatment is successful when it prevents progression (worsening) of the spinal curve during adolescence. However, physicians' understanding of what causes progression is unclear, so a method for predicting a braced patient's risk of progression could assist physicians in planning treatment. Some such models...
This paper proposes a novel way of matching general type-2 fuzzy sets using a sequence-based approach. General sequences are defined as an ordered list of objects, which are called events. In our contribution, an event of the sequenced type-2 fuzzy set is defined as the footprint of uncertainty of a specific α-plane. Suited matching algorithms for generalized sequences can be applied to this new interpretation,...
In this paper, we propose a procedure for computing the dissimilarity measure of finite general type-2 fuzzy sets, represented as sequences of vertical slices. Through representing general type-2 fuzzy sets as a sequence of objects, we compute their overall dissimilarity value using suited matching algorithms for generalized sequences. The evaluation of the proposed matching algorithm is performed...
A large number of non-dominated fuzzy rule-based classifiers are often obtained by applying a multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm to a pattern classification problem. The obtained set of non-dominated classifiers can be used to analyze their accuracy-interpretability tradeoff relation. One important issue, which has not been discussed in many studies on MoFGBML,...
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