Mostafa Sabzekar

Bio: Mostafa Sabzekar is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Support vector machine & Fuzzy logic. The author has an hindex of 6, co-authored 15 publications receiving 107 citations.

Fuzzy c-means improvement using relaxed constraints support vector machines

Abstract: Fuzzy clustering is a widely applied method for extracting the underlying models within data. It has been applied successfully in many real-world applications. Fuzzy c-means is one of the most popular fuzzy clustering methods because it produces reasonable results and its implementation is straightforward. One problem with all fuzzy clustering algorithms such as fuzzy c-means is that some data points which are assigned to some clusters have low membership values. It is possible that many samples may be assigned to a cluster with low-confidence. In this paper, an efficient and noise-aware implementation of support vector machines, namely relaxed constraints support vector machines, is used to solve the mentioned problem and improve the performance of fuzzy c-means algorithm. First, fuzzy c-means partitions data into appropriate clusters. Then, the samples with high membership values in each cluster are selected for training a multi-class relaxed constraints support vector machine classifier. Finally, the class labels of the remaining data points are predicted by the latter classifier. The performance of the proposed clustering method is evaluated by quantitative measures such as cluster entropy and Minkowski scores. Experimental results on real-life data sets show the superiority of the proposed method.

Intelligent Arrhythmia Detection Using Genetic Algorithm and Emphatic SVM (ESVM)

Abstract: In this paper, a new method of arrhythmia classification is proposed. At first we extract twenty two features from electrocardiogram signal. We propose a novel classification system based on genetic algorithm to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminate function, and looking for the best subset of features that feed the classifier. We select appropriate features with our proposed Genetic-SVM approach. We also propose Emphatic SVM (ESVM), a new SVM classifier, with fuzzy constraints. It emphasizes on constraints of SVM formulation to give more ability to our classifier. We finally, classify the ECG signal with the ESVM. Experimental results show that our proposed approach is very truthfully for diagnosing cardiac arrhythmias. Our goal is classification of four types of arrhythmias which with this method we obtain 95% correct classification.

Improved DAG SVM: A New Method for Multi-Class SVM Classification

Abstract: In this paper, we present our method which is a performance improvement to the Directed Acyclic Graph Support Vector Machines (DAG SVM). It suggests a weighted multi-class classification technique which divides the input space into several subspaces. In the training phase of the technique, for each subspace, a DAG SVM is trained and its probability density function (pdf) is guesstimated. In the test phase, fit in value of each input pattern to every subspace is calculated using the pdf of the subspace as the weight of each DAG SVM. Finally, a fusion operation is defined and applied to the DAG SVM outputs to decide the class label of the given input pattern. Evaluation results show the prominence of our method of multi-class classification compared with DAG SVM. Some data sets including synthetic one, the iris, and the wine data sets relative standard DAG SVM, were used for the evaluation.

Relaxed constraints support vector machines for noisy data

Abstract: Real-world data collected for computer-based applications are frequently impure. Differentiation of outliers and noisy data from normal ones is a major task in data mining applications. On the other hand, elimination of noisy and outlier data from training samples of a dataset may lead to over-fitting or information loss. A fuzzy support vector machine (FSVM) provides an effective means to deal with this problem. It reduces the effect of the noisy data and outliers by using a fuzzy membership functions. In this paper, a new formation for SVMs is introduced that considers importance degrees for training samples. The constraints of the SVM are converted to fuzzy inequalities. The proposed method, RSVM, shows better efficiency in the classification of data in different domains. Especially, using the proposed RSVM for multi-class classification of arrhythmia disease is presented at the end of this paper as a practical case study to show the effectiveness of the proposed system.

Relaxed constraints support vector machine

Abstract: This paper presents a new model of support vector machines (SVMs) that handle data with tolerance and uncertainty. The constraints of the SVM are converted to fuzzy inequality. Giving more relaxation to the constraints allows us to consider an importance degree for each training samples in the constraints of the SVM. The new method is called relaxed constraints support vector machines (RSVMs). Also, the fuzzy SVM model is improved with more relaxed constraints. The new model is called fuzzy RSVM. With this method, we are able to consider importance degree for training samples both in the cost function and constraints of the SVM, simultaneously. In addition, we extend our method to solve one-class classification problems. The effectiveness of the proposed method is demonstrated on artificial and real-life data sets. © 2012 Wiley Periodicals, Inc.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Classification in the Presence of Label Noise: A Survey

Abstract: Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

Expert systems

Abstract: This chapter explains the meaning of basic terms and concepts related to expert systems, describes the architecture and functions of an expert system, and the conditions under which the building or purchase of an expert system is feasible. The benefits and disadvantages attached to the use of expert systems will be analysed. Also included are a description of the tools and methods used for building expert systems plus the criteria used to evaluate and compare these tools. Finally the reader will gain an understanding of the difficult process of knowledge acquisition and an awareness of the legal implications of the use of expert systems.

Support vector machines: Theory and applications

Abstract: This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.

Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization

Abstract: We present two new hybrids of FCM and improved self-adaptive PSO.The methods are based on the FCM-PSO algorithm.We use FCM to initialize one particle to achieve better results in less iterations.The new methods are compared to FCM-PSO using many real and synthetic datasets.The proposed methods consistently outperform FCM-PSO in three evaluation metrics. Fuzzy clustering has become an important research field with many applications to real world problems. Among fuzzy clustering methods, fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency, although it shows some weaknesses, particularly its tendency to fall into local minima. To tackle this shortcoming, many optimization-based fuzzy clustering methods have been proposed in the literature. Some of these methods are based solely on a metaheuristic optimization, such as particle swarm optimization (PSO) whereas others are hybrid methods that combine a metaheuristic with a traditional partitional clustering method such as FCM. It is demonstrated in the literature that methods that hybridize PSO and FCM for clustering have an improved accuracy over traditional partitional clustering approaches. On the other hand, PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques. Another problem with PSO-based clustering is that the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. In this paper we introduce two hybrid methods for fuzzy clustering that aim to deal with these shortcomings. The methods, referred to as FCM-IDPSO and FCM2-IDPSO, combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions. Experiments using two synthetic data sets and eight real-world data sets are reported and discussed. The experiments considered the proposed methods as well as some recent PSO-based fuzzy clustering methods. The results show that the methods introduced in this paper provide comparable or in many cases better solutions than the other methods considered in the comparison and were much faster than the other state of the art PSO-based methods.