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Adaptive and Interpretable Speed Control Rules for En Route Arrival Management Using Extended learning classifier system

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IEEE ACCESS 2025年 13卷 39631-39649页

作者： Sekine, Katsuhiro Tatsukawa, Tomoaki Aoki, Takeru Fujii, Kozo Itoh, Eri Tokyo Univ Sci Dept Informat & Comp Technol Tokyo 1628601 Japan Univ Tokyo Dept Aeronaut & Astronaut Tokyo 1138654 Japan Univ Tokyo Res Ctr Adv Sci & Technol Tokyo 1538904 Japan Elect Nav Res Inst Air Traff Management Dept Tokyo 1820012 Japan

Air-transport infrastructure is facing increasing congestion, necessitating innovative approaches to optimize arrival management and minimize delays near airports. To address this issue, this study developed an en route arrival management framework that employs a learning classifier system (LCS) to reduce arrival delays and fuel consumption by generating adaptive and interpretable speed-control rules. A virtual design database, created from multiobjective optimization and integrated with a cellular automaton (CA)-based air-traffic model, facilitates the development of these rules. Within this database, a binary target variable represents speed-control decisions, whereas explanatory variables encompass factors such as aircraft proximity and congestion levels. The LCS is trained on this dataset to generate speed-control rules, and their effectiveness is evaluated through CA-based traffic simulations. The results indicate that the LCS achieved 96.8% accuracy in predicting appropriate speed-control actions, significantly outperforming traditional decision-tree methods (70.5% accuracy). Furthermore, experimental findings demonstrate reductions in average flight times by 10-20 s and up to 5 fewer spacing adjustments for traffic within 600 s before and after the arrival of speed-controlled flights. Overall, the LCS reduced total flight time by 1,801 s and total fuel consumption by 1,650.1 kg per operational cycle, resulting in an estimated annual cost savings of 466,000. Furthermore, the LCS approach successfully extracts both sector-specific and common rules, offering enhanced adaptability to various traffic scenarios. These results suggest that the ability to generalize rules across different airspace sectors can improve the safety and efficiency of air-traffic management.

关键词： Air traffic management arrival management arrival management machine learning machine learning learning classifier system learning classifier system learning classifier system

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learning classifier system ensemble and compact rule set

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CONNECTION SCIENCE 2007年第4期19卷 321-337页

作者： Gao, Yang Huang, Joshua Zhexue Wu, Lei Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Hong Kong E Business Technol Inst Hong Kong Hong Kong Peoples R China

This paper presents a learning classifier system ensemble for knowledge discovery from incremental data. The new ensemble was designed with a two-level architecture to improve the generalization ability. The new incoming cases are first bootstrapped to generate data as inputs to the first level classical learning classifier systems. The second level contains a plurality-vote module to determine the final classification by combining the classification results of the first level learning classifier systems. Each learning classifier system in the first level consists of two major modules, a genetic algorithm module for facilitating rule-discovery and a reinforcement learning module for adjusting the strength of the corresponding rules when rewards are received from the environment. We propose a revised Wilson's compact rule algorithm for generation of the compact rule set from the population set to improve the readability of the model. Two experiments were conducted. One was data mining of medical data and the other was steganalysis of images. The experimental results have shown that the new ensemble produced better performance on incremental data mining and better generalization than the single learning classifier system and other supervised learning methods. The results also showed that the compact rules were more interpretable.

关键词： learning classifier system learning classifier system ensemble compact rule set evolutionary computing reinforcement learning ensemble learning

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learning classifier system Based on Mean of Reward

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2017年第5期21卷 895-906页

作者： Tatsumi, Takato Sato, Hiroyuki Takadama, Keiki Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

This paper focuses on the generalization of classifiers in noisy problems and aims at construction learning classifier system (LCS) that can acquire the optimal classifier subset by dynamically determining the classifier generalization criteria. In this paper, an accuracy-based LCS (XCS) that uses the mean of the reward (XCS-MR) is introduced, which can correctly identify classifiers as either accurate or inaccurate for noisy problems, and investigates its effectiveness when used for several noisy problems. Applying XCS and an XCS based on the variance of reward (XCS-VR) as the conventional LCSs, along with XCS-MR, to noisy 11-multiplexer problems where the reward value changes according to a Gaussian distribution, Cauchy distribution, and lognormal distribution revealed the following: (1) XCS-VR and XCS-MR could select the correct action for every type of reward distribution;(2) XCS-MR could appropriately generalize the classifiers with the smallest amount of data;and (3) XCS-MR could acquire the optimal classifier subset in every trial for every type of reward distribution.

关键词： learning classifier system accuracy criteria reward sample standard deviation

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A convolutional neural-based learning classifier system for detecting database intrusion via insider attack

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INFORMATION SCIENCES 2020年 512卷 123-136页

作者： Bu, Seok-Jun Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

Role-based access control (RBAC) in databases provides a valuable level of abstraction to promote security administration at the business enterprise level. With the capacity for adaptation and learning, machine learning algorithms are suitable for modeling normal data access patterns based on large amounts of data and presenting robust statistical models that are not sensitive to user changes. We propose a convolutional neural-based learning classifier system (CN-LCS) that models the role of queries by combining conventional learning classifier system (LCS) with convolutional neural network (CNN) for a database intrusion detection system based on the RBAC mechanism. The combination of modified Pittsburgh-style LCSs for the optimization of feature selection rules and one-dimensional CNNs for modeling and classification in place of traditional rule generation outperforms other machine learning classifiers on a synthetic query dataset. In order to quantitatively compare the inclusion of rule generation and modeling processes in the CN-LCS, we have conducted 10-fold cross-validation tests and analysis through a paired sampled t-test. (C) 2019 Published by Elsevier Inc.

关键词： Deep learning Convolutional neural network learning classifier system Database intrusion detection

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Pittsburgh-style learning classifier system for multiple environments: towards robust waterbus route for several situations

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2011年第6期3卷 370-383页

作者： Sato, Keiji Takadama, Keiki Univ Electrocommun Dept Informat Chofu Tokyo 1828585 Japan Japan Sci & Technol Agcy JST PRESTO Kawaguchi Saitama 3320012 Japan

This paper proposes an accuracy-based Pittsburgh-style learning classifier system (LCS) that can find effective and robust solutions against several different situations, and aims at investigating its effectiveness in the waterbus route optimisation problem. For this purpose, our accuracy-based Pittsburgh-style LCS: 1 introduces a new fitness calculation to remain robust classifiers (i.e., solutions) in different situations 2 employs NSGA-II to find the most effective and robust solutions among a lot of Pareto front solutions found in the multi-objective optimisation. Through intensive simulations on the waterbus route optimisation problem, we have revealed that our proposed LCS can find the waterbus routes that can cope with two different situations. In detail: 1 the relative fitness calculation can find the robust routes in comparison with the ordinary fitness calculation 2 the accuracy-based selection of the parents succeeds to find more effective and robust route in the different environments in comparison with the NSGA-II-based selection in the multi-objective optimisation.

关键词： learning classifier system LCS accuracy multi-objective optimisation non-dominated sorting genetic algorithm NSGA-II waterbus route optimisation

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Resource management and scalability of the XCSF learning classifier system

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THEORETICAL COMPUTER SCIENCE 2012年 425卷 126-141页

作者： Stalph, Patrick O. Llora, Xavier Goldberg, David E. Butz, Martin V. Univ Wurzburg Dept Cognit Psychol D-97070 Wurzburg Germany Univ Illinois Illinois Genet Algorithm Lab Urbana IL 61801 USA

It has been shown many times that the evolutionary online learning XCS classifier system is a robustly generalizing reinforcement learning system, which also yields highly competitive results in data mining applications. The XCSF version of the system is a real-valued function approximation system, which learns piecewise overlapping local linear models to approximate an iteratively sampled function. While the theory on the binary domain side goes as far as showing that XCS can PAC learn a slightly restricted set of k-DNF problems, theory for XCSF is still rather sparse. This paper takes the theory from the XCS side and projects it onto the real-valued XCSF domain. For a set of functions, in which fitness guidance is given, we even show that XCSF scales optimally with respect to the population size, requiring only a constant overhead to ensure that the evolutionary process can locally optimize the evolving structures. Thus, we provide foundations concerning scalability and resource management for XCSF. Furthermore, we reveal dimensions of problem difficulty for XCSF - and local linear learners in general - showing how structural alignment, that is, alignment of XCSF's solution representation to the problem structure, can reduce the complexity of challenging problems by orders of magnitude. (C) 2010 Elsevier B.V. All rights reserved.

关键词： learning classifier system XCS Function approximation Scalability Resource management Structure alignment

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Energy-efficient workload allocation in fog-cloud based services of intelligent transportation systems using a learning classifier system

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IET INTELLIGENT TRANSPORT systemS 2020年第11期14卷 1484-1490页

作者： Abbasi, Mahdi Yaghoobikia, Mina Rafiee, Milad Jolfaei, Alireza Khosravi, Mohammad R. Bu Ali Sina Univ Engn Fac Dept Comp Engn Hamadan Hamadan Iran Macquarie Univ Dept Comp Sydney NSW Australia Persian Gulf Univ Dept Comp Engn Bushehr Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Nowadays, renewable energies have been considered as one of the important sources of energy supply in delay-sensitive fog computations in intelligent transportation systems due to their cheapness and availability. This study addresses the challenges of using renewable power supplies in delay-sensitive fogs and proposes an efficient workload allocation method based on a learning classifier system. The system dynamically learns the workload allocation policies between the cloud and the fog servers and then converges on the optimal allocation that fulfils the energy and delay requirements in the overall transportation system. Simulation results confirm that the proposed algorithm reduces the long-term costs of the system including service delay and operating costs. Also, compared to some other techniques, when the proposed method presents the most successful solution for reducing the average delay of the workloads and converging on the minimum value as well as retaining or even increasing the battery levels of fog nodes up to 100%. The lowest cost of the delay is 5 among other available methods, whereas in the proposed method, this value approaches 4.5.

关键词： processor scheduling resource allocation learning (artificial intelligence) power aware computing energy conservation cloud computing energy-efficient workload allocation fog-cloud based services intelligent transportation systems learning classifier system renewable energies energy supply delay-sensitive fog computations renewable power supplies delay-sensitive fogs efficient workload allocation method workload allocation policies fog servers optimal allocation transportation system system including service delay average delay fog nodes

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Operating rules classification system of water supply reservoir based on learning classifier system

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EXPERT systemS WITH APPLICATIONS 2009年第3期36卷 5654-5659页

作者： Wang, Xiao-Lin Yin, Zheng-Jie Lv, Yi-Bing Li, Si-Fu China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China Yangtze River Sci Res Inst Water Resource Dept Wuhan 430010 Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China

Genetic algorithm-based learning classifier system (LCS) is a massively parallel, message-passing and rule-based machine learning system. But its potential self-adaptive learning capability has not been paid enough attention in reservoir operation research. In this paper, an operating rule classification system based on LCS, which learns through credit assignment (the bucket brigade algorithm) and rule discovery(the genetic algorithm), is established to extract water-supply reservoir operating rules. The proposed system acquires the online identification rate 95% for training samples and offline rate 85% for testing samples in a case study, and further discussions are made about the impacts on the performances or behaviors of the rule classification system from three aspects of obtained rules, training or testing samples and the comparisons between the rule classification system and the artificial neural network (ANN). The results indicate the learning classifier system is practical and effective to obtain the reservoir supply operating rules. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Reservoir operating rules Water supply learning classifier system Genetic algorithm

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Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第4期20卷 603-612页

作者： Urbanowicz, Ryan John Andrew, Angeline S. Karagas, Margaret Rita Moore, Jason H. Dartmouth Coll Dept Genet Geisel Sch Med Lebanon NH 03756 USA

Background and objective Detecting complex patterns of association between genetic or environmental risk factors and disease risk has become an important target for epidemiological research. In particular, strategies that provide multifactor interactions or heterogeneous patterns of association can offer new insights into association studies for which traditional analytic tools have had limited success. Materials and methods To concurrently examine these phenomena, previous work has successfully considered the application of learning classifier systems (LCSs), a flexible class of evolutionary algorithms that distributes learned associations over a population of rules. Subsequent work dealt with the inherent problems of knowledge discovery and interpretation within these algorithms, allowing for the characterization of heterogeneous patterns of association. Whereas these previous advancements were evaluated using complex simulation studies, this study applied these collective works to a 'real-world' genetic epidemiology study of bladder cancer susceptibility. Results and discussion We replicated the identification of previously characterized factors that modify bladder cancer risk-namely, single nucleotide polymorphisms from a DNA repair gene, and smoking. Furthermore, we identified potentially heterogeneous groups of subjects characterized by distinct patterns of association. Cox proportional hazard models comparing clinical outcome variables between the cases of the two largest groups yielded a significant, meaningful difference in survival time in years (survivorship). A marginally significant difference in recurrence time was also noted. These results support the hypothesis that an LCS approach can offer greater insight into complex patterns of association. Conclusions This methodology appears to be well suited to the dissection of disease heterogeneity, a key component in the advancement of personalized medicine.

关键词： Bladder cancer Epistasis Heterogeneity learning classifier system Smoking XPD

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ExSTraCS 2.0: description and evaluation of a scalable learning classifier system

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EVOLUTIONARY INTELLIGENCE 2015年第2-3期8卷 89-116页

作者： Urbanowicz, Ryan J. Moore, Jason H. Univ Penn Perelman Sch Med 423 Guardian Dr Philadelphia PA 19104 USA

Algorithmic scalability is a major concern for any machine learning strategy in this age of 'big data'. A large number of potentially predictive attributes is emblematic of problems in bioinformatics, genetic epidemiology, and many other fields. Previously, ExSTraCS was introduced as an extended Michigan-style supervised learning classifier system that combined a set of powerful heuristics to successfully tackle the challenges of classification, prediction, and knowledge discovery in complex, noisy, and heterogeneous problem domains. While Michigan-style learning classifier systems are powerful and flexible learners, they are not considered to be particularly scalable. For the first time, this paper presents a complete description of the ExSTraCS algorithm and introduces an effective strategy to dramatically improve learning classifier system scalability. ExSTraCS 2.0 addresses scalability with (1) a rule specificity limit, (2) new approaches to expert knowledge guided covering and mutation mechanisms, and (3) the implementation and utilization of the TuRF algorithm for improving the quality of expert knowledge discovery in larger datasets. Performance over a complex spectrum of simulated genetic datasets demonstrated that these new mechanisms dramatically improve nearly every performance metric on datasets with 20 attributes and made it possible for ExSTraCS to reliably scale up to perform on related 200 and 2000-attribute datasets. ExSTraCS 2.0 was also able to reliably solve the 6, 11, 20, 37, 70, and 135 multiplexer problems, and did so in similar or fewer learning iterations than previously reported, with smaller finite training sets, and without using building blocks discovered from simpler multiplexer problems. Furthermore, ExSTraCS usability was made simpler through the elimination of previously critical run parameters.

关键词： learning classifier system Scalability Evolutionary algorithm Data mining Classification Prediction

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