Comparative analysis of interpolative and non-interpolative fuzzy rule based machine learning systems applying various numerical optimization methods

Balazs, Krisztian; Botzheim, János; Kóczy, László T.

doi:10.1109/fuzzy.2010.5584156

Cited by 12 publications

(6 citation statements)

References 19 publications

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“…The simulation runs have been carried out on the extensions of the fuzzy learning architectures proposed and discussed in [7] - [10]. Due to space limitations the base architectures are not explained in this paper.…”

Section: A Circumstances Of the Experimental Analysismentioning

confidence: 99%

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New parameterizable search space narrowing technique for adjusting between accuracy and interpretability in fuzzy systems

Balazs

Kóczy

2012

2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI)

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It is well known that beyond the fact that fuzzy systems have favorable modeling capabilities from the viewpoint of accuracy, they also have outstanding inherent interpretability possibilities, which is a rather unique property among modeling architectures and which is a strong motivation for their research and application. This paper focuses on both mentioned property types and proposes a new technique for adjusting between accuracy and interpretability in modeling systems where fuzzy rule based architectures together with evolutionary algorithms are used for knowledge extraction.First, an inconsistency problem of conventional interpretable fuzzy systems is resolved. Then, a new search space narrowing technique for evolutionary algorithms is proposed, which can be applied for constructing interpretable fuzzy rule bases. Finally, the favorable properties of this new approach will be verified experimentally by carrying out simulation runs.

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Section: A Circumstances Of the Experimental Analysismentioning

confidence: 99%

“…Our preceding works [7] - [10] dealt with the construction of various types of fuzzy rule based knowledge extraction architectures by applying several evolutionary optimization approaches. These researches mainly focused on the efficiency of the established systems in terms of the achieved accuracy of the extracted knowledge base.…”

Section: Introductionmentioning

confidence: 99%

New parameterizable search space narrowing technique for adjusting between accuracy and interpretability in fuzzy systems

Balazs

Kóczy

2012

2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI)

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“…[13], [14], [7]). Approaches like Genetic Programming (GProg) [16] and Bacterial Programming (BProg) [17], present an alternative to the former algorithms.…”

Section: Introductionmentioning

confidence: 95%

“…This paper focuses on this combined approach, namely, applying hierarchical-interpolative fuzzy rule bases as previous papers [7], [8], [9] considered the other ways of complexity reduction.…”

Section: Introductionmentioning

confidence: 98%

Genetic and Bacterial Memetic Programming approaches in hierarchical-interpolative fuzzy system construction

Balazs

Kóczy

2012

2012 IEEE International Conference on Fuzzy Systems

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As a straightforward continuation of our previous work in this paper new memetic (combined evolutionary and gradient based) methods are proposed for constructing hierarchicalinterpolative fuzzy rule bases in the frame of a supervised machine learning system modeling black box systems defined by input-output pairs. In this work the resulting hierarchical rule bases are constructed by using structure building Genetic and Bacterial Memetic Programming Algorithms, thus stochastic evolutionary optimization methods containing deterministic local search steps.Applying hierarchical-interpolative fuzzy rule bases has proved an efficient way of reducing the complexity of knowledge bases, whereas memetic techniques often ensure a relatively fast convergence in the learning process.The literature has highlighted the advantages of memetic methods against pure evolutionary algorithms, thus the combination of hierarchical-interpolative fuzzy rule bases with memetic techniques may form promising hierarchical-interpolative machine learning systems.

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“…Our past works [7] - [10] dealt with the construction of various types of fuzzy rule based knowledge extraction architectures by applying several evolutionary optimization approaches. These researches mainly focused on the efficiency of the established systems in terms of the achieved accuracy of the extracted knowledge base.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Model for Analyzing the Interpretability-Accuracy Trade-off in Interpretable Fuzzy Systems Using Nested Hyperball Structures

Balazs¹,

Kóczy²

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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Our recent work proposed a new meaning preservation approach together with a parameterizable nested hyperball structured search space for interpretable fuzzy systems in order to solve a problem of inconsistency observed in conventional interpretable fuzzy knowledge bases and simultaneously to address the adjustment of the trade-off between interpretability and accuracy.Based on intuitive reasonings and simulation results a conjecture was formulated about favorable trade-off adjustment properties of the proposed method.The aim of the present paper is to construct a mathematical model, in which the conjectured properties can be analyzed and formally verified. Some computational considerations about the interpretation of the resulting knowledge bases are also made.

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Comparative analysis of interpolative and non-interpolative fuzzy rule based machine learning systems applying various numerical optimization methods

Cited by 12 publications

References 19 publications

New parameterizable search space narrowing technique for adjusting between accuracy and interpretability in fuzzy systems

New parameterizable search space narrowing technique for adjusting between accuracy and interpretability in fuzzy systems

Genetic and Bacterial Memetic Programming approaches in hierarchical-interpolative fuzzy system construction

A Stochastic Model for Analyzing the Interpretability-Accuracy Trade-off in Interpretable Fuzzy Systems Using Nested Hyperball Structures

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