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Exemplar-Based learning classifier system with Dynamic Matching Range for Imbalanced Data

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2017年第5期21卷 868-875页

作者： Matsushima, Hiroyasu Takadama, Keiki Natl Inst Adv Ind Sci & Technol Tsukuba Ctr 1 1-1-1 Umezono Tsukuba Ibaraki 3058560 Japan Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

In this paper, we propose a method to improve ECS-DMR which enables appropriate output for imbalanced data sets. In order to control generalization of LCS in imbalanced data set, we propose a method of applying imbalance ratio of data set to a sigmoid function, and then, appropriately update the matching range. In comparison with our previous work (ECS-DMR), the proposed method can control the generalization of the appropriate matching range automatically to extract the exemplars that cover the given problem space, wchich consists of imbalanced data set. From the experimental results, it is suggested that the proposed method provides stable performance to imbalanced data set. The effect of the proposed method using the sigmoid function considering the data balance is shown.

关键词： learning classifier system exemplar knowledge extraction imbalanced data set single-step problems

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Customer satisfaction prediction with Michigan-style learning classifier system

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SN APPLIED SCIENCES 2019年第11期1卷 1450页

作者： Borna, Keivan Hoseini, Shokoofeh Aghaei, Mohammad Ali Mehdi Kharazmi Univ Fac Math & Comp Sci Dept Comp Sci Tehran Iran Kharazmi Univ Fac Management Dept IT Management Tehran Iran Islamic Azad Univ Dept IT Management Res & Sci Branch Tehran Iran

Many different classification algorithms can be use in order to analyze, classify and predict data. learning classifier system (LCS) which is known as a genetic base machine learning system, combines the machine learning with evolutionary computing and other heuristics to produce an adaptive system that learns to solve a particular problem. This paper uses the Michigan style LCS, in the context of bank customer satisfaction to classify customers into two different groups: unsatisfied/satisfied customers. Three different Rule Compaction strategies are used to compare the rule population's accuracy and micro/macro population size. The result specifies features that mostly influence prediction.

关键词： learning classifier system Evolutionary algorithm Michigan style LCS Machine learning Prediction

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A Phenotypic learning classifier system for Problems with Continuous Features

A Phenotypic Learning Classifier System for Problems with Co...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Liu, Yi Cui, Yu Cheng, Wen Browne, Will N. Xue, Bing Zhu, Chengyuan Zhang, Yiding Sheng, Mingkai Zeng, Lingfang Zhejiang Lab Hangzhou Zhejiang Peoples R China Queensland Univ Technol Brisbane Qld Australia Victoria Univ Wellington Wellington New Zealand HaiNan Normal Univ Haikou Hainan Peoples R China

ISBN: (纸本)9798400704949

Over the past four decades, learning classifier systems (LCSs) have faced challenges in producing accurate and interpretable models for domains with continuous features, mainly due to the irrelevance issue caused by genotypic methods. These methods directly modify genotypes (conditions), leading to the creation of irrelevant rules. Phenotypic LCSs, which first modify a rule's phenotype (covered instance set) before altering its genotype, can avoid this issue. However, previous phenotypic LCSs struggle with overfitting, resulting in lower testing performance. In response, we propose a novel phenotypic LCS featuring innovations: 1) a heterogeneous phenotype approach in the rule discovery mechanism to alleviate overfitting, and 2) Informed Mutation leverages the inherent neighbouring of similar instances to enhance rule generalization, thereby improving model interpretability. The proposed LCS demonstrates its success with superior testing performance and more interpretable models in all experiments compared to other LCSs. Notably, in a problem with 2048 features, the proposed LCS model outperformed the genotypic UCS by achieving a 97.4% testing accuracy with just 13 rules, compared to the UCS's 9961 rules but only 49.9% accuracy.

关键词： learning classifier system Mutation Absumption Subsumption

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The Bayesian learning classifier system: Implementation, Replicability, Comparison with XCSF

The Bayesian Learning Classifier System: Implementation, Rep...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Paetzel, David Haehner, Joerg Univ Augsburg Augsburg Germany

ISBN: (纸本)9781450392372

learning classifier systems (LCSs) are a family of versatile rule-based machine learning algorithms. Despite their long research history, the foundations of most LCSs are still informal due to them having been developed in an ad-hoc manner. An exception to this is the fully Bayesian LCS described by Drugowitsch in his 2008 book. In the present paper we shortly reiterate the central points of that system and then showcase our Python implementation of it, reporting on our attempt at replicating Drugowitsch's empirical results as well as on the results of a preliminary comparison study with the well-known LCS XCSF on the same learning tasks. We are able to replicate parts of Drugowitsch's results and explain the remaining differences. The comparison results make us conclude that the system may be competitive and exhibits unique features. Finally, we identify its current greatest shortcomings and based on those the next steps towards making it more universally usable.

关键词： learning classifier system Evolutionary Rule-based learning Bayesian Model Implementation Replicability

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From Extraction to Generation of Design Information Paradigm Shift in Data Mining via Evolutionary learning classifier system

From Extraction to Generation of Design Information Paradigm...

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International Conference on Computational Science (ICCS)

作者： Chiba, Kazuhisa Nakata, Masaya Univ Electrocommun Tokyo Japan Yokohama Natl Univ Yokohama Kanagawa Japan

This paper aims at generating as well as extracting design strategies for a real world problem using an evolutionary learning classifier system. Data mining for a design optimization result as a virtual database specifies design information and discovers latent design knowledge. It is essential for decision making in real world problems. Although we employed several methods from classic statistics to artificial intelligence to obtain design information from optimization results, we may not cognize anything beyond a prepared database. In this study, we have applied an evolutionary learning classifier system as a data mining technique to a real world engineering problem. Consequently, not only it extracted known design information but also it successfully generated design strategies not to extract from the database. The generated design rules do not physically become innovative knowledge because the prepared dataset include Pareto solutions owing to complete exploration to the edge of the feasible region in the optimization. However, this problem is independent of the method, our evolutionary learning classifier system is a useful method for incomplete datasets. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： design information generation evolutionary machine learning learning classifier system data mining knowledge discovery real-world application

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On the use of rule-sharing in learning classifier system ensembles

On the use of rule-sharing in learning classifier system ens...

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IEEE Congress on Evolutionary Computation

作者： Bull, L Studley, M Bagnall, T Whittley, I Univ W England Sch Comp Sci Bristol BS16 1QY Avon England

ISBN: (纸本)0780393635

This paper presents an investigation into exploiting the population-based nature of learning classifier systems for their use within highly-parallel systems. In particular, the use of simple accuracy-based learning classifier systems within the ensemble machine approach is examined. Results indicate that inclusion of a rule migration mechanism inspired by Parallel Genetic Algorithms is an effective way to improve learning speed.

关键词： genetic algorithms learning (artificial intelligence) parallel algorithms pattern classification ensemble machine learning classifier system parallel genetic algorithms population-based nature rule migration mechanism rule-sharing Genetic Algorithm (GA) Parallel algorithms Pattern recognition classifiers learning learning systems Ensembles

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Semi-Supervised learning classifier system Based on Bayes

Semi-Supervised Learning Classifier System Based on Bayes

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3rd International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2011)

作者： Li, Guoqiang Zou, Hua Yang, Fangchun Beijing Univ Posts & Telecommun Network Technol Res Inst Beijing 100876 Peoples R China

ISBN: (纸本)9783037850190

The high interpretability and the extraordinary evolvability of learning classifier system make it the optimal choice to build an adaptive intelligent system, and UCS is one of its branches, which is especially designed for the supervised learning tasks. However usually there is a huge amount of unlabeled data that are helpful for the increasing of its accuracy. Hence we use the EM algorithm in Semi-supervised learning as a reference, and proposed a Semi-Supervised classifier system (SUCS) based on Bayes inference. The experiments we did using the UCI dataset showed that SUCS performed a much better accuracy than UCS by use of only a small number of labeled data and a large number of unlabeled data.

关键词： Adaptive Interactive system learning classifier system sUpervised classifier system user model Semi-supervised learning

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Waterbus route optimization by Pittsburgh-style learning classifier system

Waterbus route optimization by Pittsburgh-style Learning Cla...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Sato, Keiji Takadama, Keiki Univ Electrocommun Dept Human Commun Tokyo 1828585 Japan

ISBN: (纸本)9784907764289

When a disaster occurs in the city center and roads and railroads etc. become unable to use, the waterbus has the great potential vehicles to transport passengers and several supplies. Since the number of passengers in such situation tend to change, according to the reconstruction degree of the city, effective and robust routes that can use two or more situations. To obtain such routes, this paper focuses on effective key routes to various situations, and proposes the method that put the pressure which decreases the number of routes. Through intensive simulations of five river stations, the following implications have been revealed, we get the routes which can transport passengers earlier than the case of not putting decreasing pressure of waterbus rout.

关键词： learning classifier system waterbus optimization generalization

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Reward allotment considered roles for learning classifier system for soccer video games

Reward allotment considered roles for learning classifier sy...

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IEEE Symposium on Computational Intelligence and Games

作者： Akatsuka, Yosuke Sato, Yuji Hosei Univ 3-7-2 Kajino Cho Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781424407095

In recent years, the video-game environment has begun to change due to the explosive growth of the Internet. As a result, it makes the time for maintenance longer and the development cost increased. In addition, the life cycle of the game program shortens. To solve the above-mentioned problem, we have already proposed the event-driven hybrid learning classifier system and showed that the system is effective to improving the game winning rate and making the learning time shorten. This paper describes the investigation result of the effect in case we apply the reward allotment considered each role for classifier learning system. Concretely, we investigate the influence to each player's actions by changing the algorithm of the opponent and to team strategy by changing reward setting, and analyze them. As a result, we show that the influence of learning effects to each player's actions does not depend on the algorithm of opponent. And we also show that the reward allotment considered each role has possible to evolve the game strategy to improving the game winning rate.

关键词： soccer game video game learning classifier system event-driven reward allotment considered roles

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Voting-XCSc: A Consensus Clustering Method via learning classifier system

Voting-XCSc: A Consensus Clustering Method via Learning Clas...

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14th International Conference on Intelligent Data Engineering and Automated learning (IDEAL)

作者： Qian, Liqiang Shi, Yinghuan Gao, Yang Yin, Hujun Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Nanjing Univ State Key Lab Novel Software & Technol Nanjing Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England

ISBN: (纸本)9783642412783;9783642412776

In this article, a novel consensus clustering method (voting-XCSc) via learning classifier system is proposed, which aims (1) to automatically determine the clustering number and (2) to achieve consensus results by reducing the influence coming from the randomness. When conducting the clustering for the data points, the proposed voting-XCSc will first employ the XCSc to generate a set of clustering results with different clustering numbers, and then it will adopt the dissociation-based strategy to experimentally determine the clustering number among all the candidates. Finally, a majority voting-based consensus method is applied to obtain the final clustering results. The proposed voting-XCSc has been evaluated on both the toy examples as well as two real clustering-related applications. i.e, lung cancer image identification, image segmentation. The results demonstrate the voting-XCSc can obtain the superior performance compared with XCSc, K-means, and other state-of-the-arts.

关键词： learning classifier system Consensus Clustering Reinforcement learning

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