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检索条件"主题词=Learning Classifier System"

共 124 条记录，以下是81-90 订阅

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Towards Final Rule Set Reduction in XCS: A Fuzzy Representation Approach 11

Towards Final Rule Set Reduction in XCS: A Fuzzy Representat...

引用

13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Shoeleh, Farzaneh Hamzeh, Ali Hashemi, Sattar Shiraz Univ Dept Comp Sci & Engn Shiraz Iran

ISBN: (纸本)9781450305570

Generalization is the most challenging issue in XCS research area. One of the main components of XCS managing to remedy this issue is knowledge representation. In this paper, a knowledge representation based on fuzzy membership function offering certain and vague regions is described. We extend the Michigan learning classifier system using this approach to be improved in terms of both performance and interpretability. The contribution of this paper is three-folds: 1) updating main parameters of classifiers based on their certainty factor in matching of incoming data, 2) enhancing essential components of XCS to be compatible with such fuzzy representation schema and 3) proposing a novel rule set reduction method named Reduction based on Least Reward Prediction (RLRP) to improve the interpretability of the evolved model. Furthermore, an inference methodology which is compatible with RLRP is suggested to maintain the similar performance. The obtained results are promising due to the effectiveness of proposed method in dealing with real world problems. Furthermore, the proposed reduction method can upgrade the interpretability of final rule set by boiling its size down by 94% on average while slightly degrading the prediction accuracy.

关键词： Knowledge Representation learning classifier system Reduction Method XCS

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Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS

Local Covering: Adaptive Rule Generation Method Using Existi...

引用

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Tadokoro, Masakazu Hasegawa, Satoshi Tatsumi, Takato Sato, Hiroyuki Takadama, Keiki Univ Electrocommun Tokyo Japan

ISBN: (纸本)9781728169293

This paper focuses on the covering mechanism in learning classifier system (LCS) which generates a new classifier (i.e., an if-then rule) when no classifier matches the input. We propose Local Covering, a niche-based rule generation method for XCS, to reduce the sensitivity of a hyperparameter that determines the generalization probability of initial rules. In Local Covering, the system looks up classifiers in the population and finds the closest match from the input. After that, the selected classifier is copied as the covering classifier and its condition is generalized to cover the input. To integrate the Local Covering method with XCS, XCS-LCPCI (XCS with Local Covering for Previously Covered Inputs) is proposed, which executes Local Covering only if the input has been previously covered by the covering processes. The experimental results with three different problems show that XCS-LCPCI successfully acquires the general classifiers with any of six different parameter settings, while the performance of the conventional XCS varies depending on the parameter settings.

关键词： learning classifier system XCS covering MNIST problem

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An anticipatory approach to improve XCSF 06

An anticipatory approach to improve XCSF

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8th Annual Genetic and Evolutionary Computation Conference

作者： Nikanjam, Amin Rahmani, Adel Iran Univ Sci & Technol Dept Comp Engn Tehran *** Iran

ISBN: (纸本)9781595931863

XCSF is a novel version of learning classifier systems (LCS) which extends the typical concept of LCS by introducing computable classifier prediction. In XCSF classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. learning process takes place using a weight update mechanism. Initial results show that XCSF can be used to evolve accurate approximations of some functions. In this paper, we try to add an anticipatory component to XCSF improving its performance.

关键词： learning classifier system anticipatory classifier system XCSF function approximation

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Archives-holding XCS classifier system: A Preliminary Study 6

Archives-holding XCS Classifier System: A Preliminary Study

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6th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Komine, Takahiro Nakata, Masaya Takadama, Keiki Univ Electrocommun Dept Informat Tokyo Japan

ISBN: (纸本)9781479959372

In dynamic environment, learning classifier system (LCS) evolves classifiers to fit the current situation, but may forget classifiers which were useful for previous situations. Our main idea is that, we store the forgotten classifiers as archives and generate new classifiers by recombining them to fit the current situation. Specifically, we propose an archive-based LCS called Arc-XCS, which detects environmental changes and generates classifiers based on the archive. The experimental results on the benchmark problem show that, Arc-XCS successfully stored good classifiers when each environmental changes occurs;compared to the conventional LCS (XCS), Arc-XCS reaches better performances with fewer trainings.

关键词： learning classifier system dynamic environment genetic algorithm reinforcement learning single-step problem generalization

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Combining Software and Hardware LCS for Lightweight On-Chip learning 7th

Combining Software and Hardware LCS for Lightweight On-Chip ...

引用

IFIP/SEC Conference as part of the 21st IFIP World Computer Congress (WCC)

作者： Bernauer, Andreas Zeppenfeld, Johannes Bringmann, Oliver Herkersdorf, Andreas Rosenstiel, Wolfgang Univ Tubingen D-72076 Tubingen Germany Tech Univ Munich D-80290 Munich Germany Forschungszentrum Informat D-76131 Karlsruhe Germany

ISBN: (纸本)9783642152337

In this paper we present a novel two-stage method to realize a lightweight but very capable hardware implementation of a learning classifier system for on-chip learning. learning classifier systems (LCS) allow taking good run-time decisions, but current hardware implementations are either large or have limited learning capabilities. In this work, we combine the capabilities of a software-based LCS, the XCS, with a lightweight hardware implementation, the LCT, retaining the benefits of both. We compare our method with other LCS implementations using the multiplexer problem and evaluate it with two chip-related problems, run-time task allocation and SoC component parameterization. In all three problem sets, we find that the learning and self-adaptation capabilities are comparable to a full-fledged system, but with the added benefits of a lightweight hardware implementation, namely small area size and quick response time. Given our work, autonomous chips based on learning classifier systems become feasible.

关键词： system-on-Chip learning classifier system XCS

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Flexible classifier selection for accuracy-based classifier systems

Flexible classifier selection for accuracy-based classifier ...

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International Multiconference of Engineers and Computer Scientists

作者： Aryan, Mashall Hashemi, Sattar Analoui, Morteza Iran Univ Sci & Technol Tehran Iran

ISBN: (纸本)9789889867140

The XCS classifier system has shown to solve typical classification problems competitively to other machine learning algorithms. This paper is to introduce a flexible classifier selection method in which mach-set generation is performed with respect to the system performance during the training. It also presents a complementary part for the Search Component of the classifier system in order to create new classifiers regarding the input message from the environment, and previous knowledge. The modified XCS learns more reliable and performs obviously better than traditional XCS.

关键词： data mining learning classifier system XCS

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Theoretical Adaptation of Multiple Rule-Generation in XCS 18

Theoretical Adaptation of Multiple Rule-Generation in XCS

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Nakata, Masaya Browne, Will Hamagami, Tomoki Yokohama Natl Univ Tokiwadai 79-5 Yokohama Kanagawa Japan Victoria Univ Wellington Wellington 6140 New Zealand

ISBN: (纸本)9781450356183

Most versions of the XCS classifier system have been designed to evolve only two rules for each rule discovery invocation, which restricts the search capacity. A difficulty behind generating multiple rules each time is the increase in the probability of deleting immature rules, which conflicts with the requirement that parent rules be sufficiently updated so that fitness represents worth. Thus the aim of this paper is to argue how XCS determines when rules can be deleted safely. The objectives are to certainly identify inaccurate rules and then to maximize how many rules XCS can generate. The proposed method enables adaptation of rulegeneration that maximizes the number of generated rules, under the assumption that the reliably inaccurate rules can be replaced with new rules. Experiments show our modification strongly improves the XCS performance on large scale problems, since it can take advantage of multi-point search more efficiently. For example, on the 135-bit multiplexer problem, XCS with our modification requires 1.57 million less training inputs compared with the standard XCS while utilizing the same number of final rules.

关键词： learning classifier system rule-generation theory

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Online, GA based Mixture of Experts: a Probabilistic Model of UCS 11

Online, GA based Mixture of Experts: a Probabilistic Model o...

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Edakunni, Narayanan U. Brown, Gavin Kovacs, Tim Univ Bristol Dept Comp Sci Bristol BS8 1TH Avon England Univ Manchester Sch Comp Sci Manchester Lancs England

ISBN: (纸本)9781450305570

In recent years there have been efforts to develop a probabilistic framework to explain the workings of a learning classifier system. This direction of research has met with limited success due to the intractability of complicated heuristic training rules used by the learning classifier systems. In this paper, we derive a learning classifier system from a mixture of experts that is similar to a sUpervised classifier system (UCS) in terms of its training and prediction routines. We start by framing the learning model as a mixture of experts which uses an Expectation Maximisation (EM) procedure to learn its parameters. The batch updates of the EM is then converted into online updates and finally into a GA based sampled online update thus ending up with a classifier system similar to a sUpervised classifier system. In this paper, we show the effectiveness of such a system as compared to UCS through a series of comparative studies on test datasets.

关键词： learning classifier system probabilistic modeling mixture of experts UCS

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Convergence Analysis of Rule-Generality on the XCS classifier system

Convergence Analysis of Rule-Generality on the XCS Classifie...

引用

2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Nakamura, Yoshiki Horiuchi, Motoki Nakata, Masaya Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9781450383509

The XCS classifier system adaptively controls a rule-generality of a rule-condition through a rule-discovery process. However, there is no proof that the rule-generality can eventually converge to its optimum value even under some ideal assumptions. This paper conducts a convergence analysis of the rule-generality on the rule-discovery process with the ternary alphabet coding. Our analysis provides the first proof that an average rule-generality of rules in a population can converge to its optimum value under some assumptions. This proof can be used to mathematically conclude that the XCS framework has a natural pressure to explore rules toward optimum rules if XCS satisfies our derived conditions. In addition, our theoretical result returns a rough setting-up guideline for the maximum population size, the mutation rate, and the GA threshold, improving the convergence speed of the rule-generality and the XCS performance.

关键词： learning classifier system rule-generality convergence analysis

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LCS-Based Automatic Configuration of Approximate Computing Parameters for FPGA system Designs 19

LCS-Based Automatic Configuration of Approximate Computing P...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Conrady, Simon Manuel, Manu Kreddig, Arne Stechele, Walter Arnold & Richter Cine Tech Munich Germany Tech Univ Munich Munich Germany SmartRay GmbH Wolfratshausen Germany

ISBN: (纸本)9781450367486

In application domains like data analysis or image processing, ever-increasing performance demands push the capabilities of computational systems to their limits. With technology scaling plateauing out, engineers are forced to rethink their approach to system design. The research field of approximate computing provides a new design paradigm which trades off accuracy against computational resources. In a complex system, multiple approximation methods can be combined to maximize the resulting benefits, but because of error propagation in the system, doing this in a controlled manner is challenging. To solve this problem, we propose to use concepts developed in the field of evolutionary machine learning to optimize approximation parameters, focusing on systems implemented on FPGA hardware. Our approach uses the rules of a learning classifier system to adjust approximation parameters. The resulting effects on both the application quality and resource usage are estimated on-the-fly and fed back to the rules with every fitness update, allowing the system to be carefully tuned to specific design goals. We illustrate the application of the proposed system to a real-world image processing problem and highlight some practical implications. As this is work in progress, we outline remaining open questions and future directions for our research.

关键词： learning classifier system Approximate Computing

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