Manish Sarkar

A clustering algorithm using an evolutionary programming-based approach

In this paper, an evolutionary programming-based clustering algorithm is proposed. The algorithm effectively groups a given set of data into an optimum number of clusters. The proposed method is applicable for clustering tasks where clusters are crisp and spherical. This algorithm determines the number of clusters and the cluster centers in such a way that locally optimal solutions are avoided. The result of the algorithm does not depend critically on the choice of the initial cluster centers.

Fuzzy-rough nearest neighbors algorithm

In this paper the classification efficiency of the conventional K-nearest neighbors algorithm is enhanced by exploiting the fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbors algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the generated class confidence values, which are interpreted in terms of the fuzzy-rough ownership values, do not necessarily summed up to one. Consequently, the proposed algorithm can distinguish between equal evidence and ignorance, and thus makes the semantics of the class confidence values richer.

Characterization of medical time series using fuzzy similarity-based fractal dimensions

This paper attempts to characterize medical time series using fractal dimensions. Existing fractal dimensions like box, information and correlation dimensions characterize the time series by measuring the rate at which the distribution of the time series changes when the length (or radius) of the box (or hypersphere) is changed. However, the measured dimensions significantly vary when the box (or hypersphere) position is changed slightly. It happens because the data points just outside the box (or hypersphere) are not accounted for, and all the data points inside the box or hypersphere are treated equally. To overcome these problems, the hypersphere is converted to a Gaussian, and thus the hard boundary becomes soft. The Gaussian represents the fuzzy similarity between the neighbors and the point around which the Gaussian is constructed. This concept of similarity is exploited to propose a fuzzy similarity-based fractal dimension. The proposed dimension aims to capture the regularity of the time series in terms of how the fuzzy similarity scales up/down when the resolution of the time series is decreased/increased. Experiments on intensive care unit (ICU) data sets show that the proposed dimension characterizes the time series better than the correlation dimension.

Modular pattern classifiers: a brief survey

While solving a complex pattern classification problem, it is often difficult to design a monolithic classifier. One approach is to divide the problem into smaller ones, and solve each subproblem using a simpler classifier. This kind of divide and conquer policy has motivated the researchers to substitute a modular classifier for the single monolithic classifier. The paper reviews the advantages, issues and various techniques available for designing the modular classifiers.

Rough-fuzzy membership functions

This paper generalizes the concept of rough membership functions in pattern classification tasks to rough-fuzzy membership functions. Unlike the rough membership value of a pattern, which is sensitive only towards the rough uncertainty associated with the pattern, the rough-fuzzy membership value of the pattern signifies the rough uncertainty as well as the fuzzy uncertainty associated with the pattern. In this paper, various set theoretic properties of the rough-fuzzy membership functions are exploited to characterize the concept of rough-fuzzy sets and to measure the rough-fuzzy ambiguity associated with a given output class. Finally, a few possible applications of the rough-fuzzy membership functions are mentioned.

Backpropagation learning algorithms for classification with fuzzy mean square error

Abstract Most of the real life classification problems have ill defined, imprecise or fuzzy class boundaries. Feedforward neural networks with conventional backpropagation learning algorithm are not tailored to this kind of classification problem. Hence, in this paper, feedforward neural networks, that use backpropagation learning algorithm with fuzzy objective functions, are investigated. A learning algorithm is proposed that minimizes an error term, which reflects the fuzzy classification from the point of view of possibilistic approach. Since the proposed algorithm has possibilistic classification ability, it can encompass different backpropagation learning algorithm based on crisp and constrained fuzzy classification. The efficacy of the proposed scheme is demonstrated on a vowel classification problem.

Fuzzy-rough neural networks for vowel classification

In many real life applications two patterns from the same cluster belong to different classes, and hence, classification based on mere similarity property is inadequate. This problem arises because the available features are not sufficient to discriminate the classes. It implies that the fuzzy clusters generated by the input features have rough uncertainty. This paper proposes a fuzzy-rough set based network which exploits fuzzy-rough membership functions to reduce this problem. The proposed network is theoretically a powerful classifier as it is equivalent to a universal approximator. Moreover, its activity is transparent as it can easily be mapped to a Takagi-Sugeno type fuzzy rule base system. The efficacy of the proposed method is studied on a vowel recognition problem.

Feedforward neural networks configuration using evolutionary programming

This paper proposes an evolutionary programming based neural network construction algorithm, that efficiently configures feedforward neural networks in terms of optimum structure and optimum parameter set. The proposed method determines the appropriate structure, i.e. an appropriate number of hidden nodes, in such a way that locally optimal solutions are avoided. While choosing the number of hidden nodes, this method performs a trade-off between generalization and memorization. In this method, the network is evolved so that it learns an optimum parameter set, i.e. weights and bias, without being trapped into a locally optimal solution. Efficiency of this method is further enhanced by incorporating the concepts of adaptive structural mutation. Finally, efficacy of the proposed scheme is demonstrated on a Contract Bridge game opening bid problem.

Fuzzy-rough membership functions

This paper generalizes the concepts of rough membership functions in pattern classification tasks to fuzzy-rough membership functions. Unlike the rough membership value of a pattern, which is sensitive only towards the rough uncertainty associated with the pattern, the fuzzy-rough membership value of the pattern signifies the rough uncertainty as well as the fuzzy uncertainty associated it. In absence of fuzziness, the fuzzy-rough membership functions reduce to the existing rough membership functions. Moreover, under certain conditions the fuzzy-rough membership functions are equivalent to fuzzy membership functions or characteristic functions. In this paper, various set theoretic properties of the fuzzy-rough membership functions are exploited to characterize the concept of fuzzy-rough sets. Some measures of the fuzzy-rough ambiguity associated with a given output class are also discussed.

Application of fuzzy-rough sets in modular neural networks

In a modular neural network, the conflicting information supplied by the information sources, i.e., the outputs of the subnetworks, can be combined by applying the concept of fuzzy integral. To compute the fuzzy integral it is essential to know the importance of each subset of the information sources in a quantified form. In practice, it is very difficult to determine the level of the information sources. However, in the fuzzy integral approach the importance of a particular information source is considered to be independent of the other information sources. Therefore, determination of the importance of each information source should be based on the incomplete knowledge supplied by the source itself. This paper proposes a fuzzy-rough set theoretic approach to find the importance of each subset of the information sources from this incomplete knowledge.