Efficient and interpretable fuzzy classifiers from data with support vector learning

Papadimitriou, Stergios; Terzidis, Konstantinos

doi:10.3233/ida-2005-9603

Cited by 11 publications

(5 citation statements)

References 31 publications

(17 reference statements)

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“…The problem of extracting fuzzy rules from trained classification SVMs models has been of interest to many researchers through the last decade. Our model is based at the work of [12]. The proposed methodology initially deploys the technique which was proposed by [13].…”

Section: Esvm Fuzzy Inference Tradermentioning

confidence: 99%

Integrating High Volume Financial Datasets to Achieve Profitable and Interpretable Short Term Trading with the FTSE100 Index

Amorgianiotis

Theofilatos

Georgopoulos

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 4: MT4BD WorkshopInternational audienceDuring the financial crisis of 2009 traditional models have failed to provide satisfactory results. Lately many techniques have been proposed to overcome the deficiencies of traditional models but most of them deal with the examined financial indices as they are cut off from the rest global market. However, many late studies are indicating that such dependencies exist. The enormous number of the potential financial time series which could be integrated to trade a single financial index enables the characterization of this problem as a “big data” problem and raises the need for advanced dimensionality reduction techniques which should additionally be interpretable in order to extract meaningful conclusions. In the present paper, ESVM-Fuzzy Inference Trader is introduced. This technique is based on the hybrid methodology ESVM Fuzzy Inference which combines genetic algorithms and some deterministic methods to extract interpretable fuzzy rules from SVM classification models.The ESVM-Fuzzy Inference Trader was applied to the task of modeling and trading the FTSE100 index using a plethora of inputs including the closing prices of various European indexes. Its experimental results were compared with a state of the art hybrid technique which combines genetic algorithm with Multilayer Perceptron Neural Networks and indicated the superiority of ESVM-Fuzzy Inference Trader. Moreover, the proposed method extracted a compact set of fuzzy trading rules which among others can be utilized to describe the dependencies between other financial indices and FTSE100 index

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Section: Esvm Fuzzy Inference Tradermentioning

confidence: 99%

Integrating High Volume Financial Datasets to Achieve Profitable and Interpretable Short Term Trading with the FTSE100 Index

Amorgianiotis

Theofilatos

Georgopoulos

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…Repeating the same arguments as previously described, in going from Eqs (11) to (13), obtains the SSVM with a nonlinear kernel as follows:…”

Section: Smooth Support Vector Machinesmentioning

confidence: 99%

“…Chen [2] investigated the connection between SVMs and additive fuzzy rule-based classification systems and proposed a learning algorithm for positive definite fuzzy classifiers (PDFCs). Based on Chen's approach, Papadimitriou [13] proposed a support vector fuzzy inference system and an interpretable rule system on top of it. In Chen's algorithm, membership functions for the same input variable are generated from location transformation of a reference function [2,4].…”

Section: Introductionmentioning

confidence: 99%

Smooth support vector learning for fuzzy rule-based classification systems

Yang

2013

IDA

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This paper extends the previous work in the connection between fuzzy classifiers and kernel machines [2] to a general case. In [2], all membership functions for the same input variable are generated from location transformation of a reference function. A translation invariant kernel is constructed from reference functions. The kernel is a Mercer kernel if the reference functions are positive definite. A support vector learning approach for positive definite fuzzy classifiers (PDFCs) was proposed.In this paper, a smooth support vector learning algorithm for fuzzy rule-based classification systems is proposed. The smooth support vector machine (SSVM) is capable of generating nonlinear separating surfaces using arbitrary kernels. The positive definiteness requirement on reference functions is relaxed. A fuzzy classifier using arbitrary reference functions can be built from the training samples based on an SSVM. The resulting fuzzy classifier is called standard binary fuzzy classifier (SBFC). Fuzzy rules are extracted with each rule given by a training sample. The reduced kernel technique is introduced to simplify the decision function of the SBFC and to reduce the number of fuzzy rules. Finally, the performance of SBFCs with different reference functions is illustrated by experimental results. Comparisons with PDFCs are also provided.

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“…The adaptive neuro-fuzzy inference system is capable of approximating any real continuous function in a compact set to any degree of accuracy [27,28]. Specifically, the ANFIS system is functionally equivalent to the Sugeno first-order fuzzy model [9].…”

Section: Anfis Classifiermentioning

confidence: 99%

Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis

Huang¹,

Hung²,

Lee³

et al. 2010

J Med Syst

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Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.

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Efficient and interpretable fuzzy classifiers from data with support vector learning

Cited by 11 publications

References 31 publications

Integrating High Volume Financial Datasets to Achieve Profitable and Interpretable Short Term Trading with the FTSE100 Index

Integrating High Volume Financial Datasets to Achieve Profitable and Interpretable Short Term Trading with the FTSE100 Index

Smooth support vector learning for fuzzy rule-based classification systems

Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis

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