Accuracy-based Learning Classifier System Research Articles

Ensemble learning for multi-channel sleep stage classification

Sleep is a vital process for human well-being. Sleep scoring is performed by experts using polysomnograms, that record several body activities, such as electroencephalograms (EEG), electrooculograms (EOG), and electromyograms (EMG). This task is known to be exhausting, biased, time-consuming, and prone to errors. Current automatic sleep scoring approaches are mostly based on single-channel EEG and do not produce explainable results. Therefore, we propose a heterogeneous ensemble learning-based approach where we combine accuracy-based learning classifier systems with different algorithms to produce a robust, explainable, and enhanced classifier. The efficiency of our approach was evaluated using the Sleep-EDF benchmark dataset. The proposed models have reached an accuracy of 89.2% for the stacking model and 87.9% for the voting model, on a multi-class classification task based on the R&K guidelines.

Theoretical Analysis of Accuracy-Based Fitness on Learning Classifier Systems

Like most evolutionary algorithms, accuracy-based learning classifier systems (XCSs) use a fitness metric to recognize the superiority of rules, under a principle that a higher-quality rule has a higher fitness. However, XCS must learn the fitness values under a reinforcement learning scheme. This introduces uncertainty and asynchrony to the fitness estimation while no theoretical work formally guarantees that such a basic principle would hold. The goal of this paper is to complement this fundamental lack in the reliability of XCS by mathematically analyzing its fitness-update scheme. Our main assumption is that the fitness is updated with an absolute accuracy instead of its relative accuracy to sidestep unpredictable dynamics of XCS. Our theoretical conclusion is that the superiority of rules can be correctly recognized through the accuracy-based fitness under finite update times. We further show that recognizing the superiority among low-quality rules is a costly procedure that increases the number of necessary rule-trainings. This drawback indicates that XCS may struggle to identify good parent rules at early generations, degrading the efficiency of evolutionary propagation.

Two-Step Markov Update Algorithm for Accuracy-Based Learning Classifier Systems

Two-Step Markov Update Algorithm for Accuracy-Based Learning Classifier Systems

Learning Classifier System Based on Mean of Reward

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.

Accuracy-Based Learning Classifier Systems for Multistep Reinforcement Learning: A Fuzzy Logic Approach to Handling Continuous Inputs and Learning Continuous Actions

Despite their proven effectiveness, many Michigan learning classifier systems (LCSs) cannot perform multistep reinforcement learning in continuous spaces. To meet this technical challenge, some LCSs have been designed to learn fuzzy logic rules. They can be largely classified into strength-based and accuracy-based systems. The latter is gaining more research attention in the last decade. However, existing accuracy-based learning systems either address primarily single-step learning problems or require the action space to be discrete. In this paper, a new accuracy-based learning fuzzy classifier system (LFCS) is developed to explicitly handle continuous state input and continuous action output during multistep reinforcement learning. Several technical improvements have been achieved while developing the new learning algorithm. Particularly, we have successfully extended ${Q}$ -learning like credit assignment methods to continuous spaces. To enable direct learning of stochastic strategies for action selection, we have also proposed to use a new fuzzy logic system with stochastic action outputs. Moreover, fine-grained learning of fuzzy rules has been achieved effectively in our algorithm by using a natural gradient learning method. It is the first time that these techniques are utilized substantially in any accuracy-based LFCSs. Meanwhile, in comparison with several recently proposed learning algorithms, our algorithm is shown to perform highly competitively on four benchmark learning problems and a robotics problem. The practical usefulness of our algorithm is also demonstrated by improving the performance of a wireless body area network.

Human-Interpretable Feature Pattern Classification System Using Learning Classifier Systems

Image pattern classification is a challenging task due to the large search space of pixel data. Supervised and subsymbolic approaches have proven accurate in learning a problem's classes. However, in the complex image recognition domain, there is a need for investigation of learning techniques that allow humans to interpret the learned rules in order to gain an insight about the problem. Learning classifier systems (LCSs) are a machine learning technique that have been minimally explored for image classification. This work has developed the feature pattern classification system (FPCS) framework by adopting Haar-like features from the image recognition domain for feature extraction. The FPCS integrates Haar-like features with XCS, which is an accuracy-based LCS. A major contribution of this work is that the developed framework is capable of producing human-interpretable rules. The FPCS system achieved 91 [Formula: see text] 1% accuracy on the unseen test set of the MNIST dataset. In addition, the FPCS is capable of autonomously adjusting the rotation angle in unaligned images. This rotation adjustment raised the accuracy of FPCS to 95%. Although the performance is competitive with equivalent approaches, this was not as accurate as subsymbolic approaches on this dataset. However, the benefit of the interpretability of rules produced by FPCS enabled us to identify the distribution of the learned angles-a normal distribution around [Formula: see text]-which would have been very difficult in subsymbolic approaches. The analyzable nature of FPCS is anticipated to be beneficial in domains such as speed sign recognition, where underlying reasoning and confidence of recognition needs to be human interpretable.

Swarm robots reinforcement learning convergence accuracy-based learning classifier systems with gradient descent (XCS-GD)

This paper presented a novel approach accuracy-based learning classifier system with gradient descent (XCS-GD) to research on swarm robots reinforcement learning convergence. XCS-GD combines covering operator and genetic algorithm. XCS-GD is responsible for adjusting precision and reducing search space according to some reward obtained from the environment, XCS-GD's innovation discovery component is responsible for discovering new better reinforcement learning rules. The experiment and simulation showed that XCS-GD approach can achieve convergence very quickly in swarm robots reinforcement learning.

Learning complex, overlapping and niche imbalance Boolean problems using XCS-based classifier systems

XCS is an accuracy-based learning classifier system, which has been successfully applied to learn various classification and function approximation problems. Recently, it has been reported that XCS cannot learn overlapping and niche imbalance problems using the typical experimental setup. This paper describes two approaches to learn these complex problems: firstly, tune the parameters and adjust the methods of standard XCS specifically for such problems. Secondly, apply an advanced variation of XCS. Specifically, we developed previously an XCS with code-fragment actions, named XCSCFA, which has a more flexible genetic programming like encoding and explicit state-action mapping through computed actions. This approach is examined and compared with standard XCS on six complex Boolean datasets, which include overlapping and niche imbalance problems. The results indicate that to learn overlapping and niche imbalance problems using XCS, it is beneficial to either deactivate action set subsumption or use a relatively high subsumption threshold and a small error threshold. The XCSCFA approach successfully solved the tested complex, overlapping and niche imbalance problems without parameter tuning, because of the rich alphabet, inconsistent actions and especially the redundancy provided by the code-fragment actions. The major contribution of the work presented here is overcoming the identified problem in the wide-spread XCS technique.

Strength-based learning classifier systems revisited: Effective rule evolution in supervised classification tasks

Learning classifier systems (LCS) are machine learning systems designed to work for both multi-step and single-step decision tasks. The latter case presents an interesting challenge for such algorithms, especially when they are applied to real-world data mining (DM) problems. The present investigation departs from the popular approach of applying accuracy-based LCS to single-step classification and aims to uncover the potential of strength-based LCS in such tasks. Although the latter family of algorithms have often been associated with poor generalization and performance, we aim at alleviating these problems by defining appropriate extensions to the traditional strength-based LCS framework. These extensions are detailed and their effect on system performance is studied through the application of the proposed algorithm on a set of artificial problems, designed to challenge its scalability and generalization abilities. The comparison of the proposed algorithm with UCS, its state-of-the-art accuracy-based counterpart, emphasizes the effects of our extended strength-based approach and validates its competitiveness in multi-class problems with various class distributions. Overall, our work presents an investigation of strength-based LCS in the domain of supervised classification. Our extensive analysis of the learning dynamics involved in these systems provides proof of their potential as real-world DM tools, inducing tractable rule-based classification models, even in the presence of severe class imbalances.

On principal component analysis for high-dimensional XCSR

XCSR is an accuracy-based learning classifier system which can handle classification problems with real-value features. However, as the number of features increases, a high classification accuracy comes at the cost of more resources: larger population sizes and longer computational running times. In this paper we investigate PCA-XCSR (a sequential application of PCA and XCSR) in three environments with different characteristics: a discrete and imbalanced environment (KDD’99 network intrusion), a continuous and highly symmetric environment (MiniBooNE), and a highly discrete, highly imbalanced environment (Census/Income (KDD)). These experiments show that in the three different environments, PCA-XCSR, in addition to being able to reduce the computational resources and time requirements of XCSR by approximately 50 %, is able to consistently maintain its high accuracy. In addition to that, it reduces the required population size needed by XCSR. Also, we suggest heuristics for selecting the number of principal components to use when using PCA-XCSR.

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

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.

Canal Structure Automation Rules Using an Accuracy-Based Learning Classifier System, a Genetic Algorithm, and a Hydraulic Simulation Model. I: Design

An accuracy-based learning classifier system (XCS), as described in a companion paper (Part I: Design), was developed and evaluated to produce operational rules for canal gate structures. The XCS w...

USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS

Historically, the artificial intelligence (AI) of interactive virtual simulations or games is usually driven by pre-defined static scripts. One of the disadvantages of such scripted opponents is that they can be deciphered and countered by an intelligent user. Thus, the user has the opportunity to find weaknesses and an easy solution against the virtual simulation, which diminishes the efficiency aspect of a training session or entertaining value drastically. While randomization can be used to mask the static behaviour of a scripted AI, it is possible to develop much richer solutions by applying Learning Classifier System (LCS) techniques to create agents with intelligent-like behaviors. Learning Classifier Systems are rule-based machine learning techniques that rely on a Genetic Algorithm to discover a knowledge map used to classify an input space into a set of actions.In this paper, we propose the use of an unsupervised machine learning technique called Accuracy-based Learning Classifier Systems (XCS) for adaptable strategy generation that can be used in virtual simulations or games. XCS use a Genetic Algorithm to evolve a knowledge base in the form of rules. The performance and adaptability of the strategies and tactics developed with the XCS is analyzed by facing these against scripted opponents on a real time strategy game. According to our experiments, the rulesets are able to adapt to a wide array of behaviors from its opponents, as opposed to a linear game script, which is limited in its ability to adapt to its environment.

Fuzzy-UCS: A Michigan-Style Learning Fuzzy-Classifier System for Supervised Learning

This paper presents Fuzzy-UCS, a Michigan-style learning fuzzy-classifier system specifically designed for supervised learning tasks. Fuzzy-UCS is inspired by UCS, an on-line accuracy-based learning classifier system. Fuzzy-UCS introduces a linguistic representation of the rules with the aim of evolving more readable rule sets, while maintaining similar performance and generalization capabilities to those presented by UCS. The behavior of Fuzzy-UCS is analyzed in detail from several perspectives. The granularity of the linguistic fuzzy representation to define complex decision boundaries is illustrated graphically, and the test performance obtained with different inference schemes is studied. Fuzzy-UCS is also compared with a large set of other fuzzy and nonfuzzy learners, demonstrating the competitiveness of its on-line architecture in terms of performance and interpretability. Finally, the paper shows the advantages obtained when Fuzzy-UCS is applied to learn fuzzy models from large volumes of data.

Learning from Data using XCS

In this paper, we present first of all the working principles of an accuracy based learning classifier system. We also discuss the use of learning classifier systems for learning from data by considering a sample application. The sample application, the Terrain Reasoner Weight Adapter (TRWA), is a system that learns near optimal weights to be used by a path planner while generating routes. Manually generated weights are used to generate a sample data set for training the TRWA. We detail the TRWA and the significant improvements made to the usual XCS strategies in order to achieve our goal of using a supervised learning technique for the TRWA. A reward assignment scheme is developed. The use of tournament selection instead of roulette wheel selection for selecting two parents in the GA is also analyzed. The results obtained show the efficiency of the method.

Learning Classifier System Ensembles With Rule-Sharing

This paper presents an investigation into exploiting the population-based nature of learning classifier systems (LCSs) for their use within highly parallel systems. In particular, the use of simple payoff and accuracy-based LCSs 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 in comparison to equivalent single systems. Presentation of a mechanism which exploits the underlying niche-based generalization mechanism of accuracy-based systems is then shown to further improve their performance, particularly, as task complexity increases. This is not found to be the case for payoff-based systems. Finally, considerably better than linear speedup is demonstrated with the accuracy-based systems on a version of the well-known Boolean logic benchmark task used throughout.

Accuracy-based learning classifier systems: models, analysis and applications to classification tasks.

Recently, Learning Classifier Systems (LCS) and particularly XCS have arisen as promising methods for classification tasks and data mining. This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems. Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently. We also investigate how the fitness pressure guides the search towards accurate classifiers. While XCS bases fitness on a reinforcement learning scheme, UCS defines fitness from a supervised learning scheme. We find significant differences in how the fitness pressure leads towards accuracy, and suggest the use of a supervised approach specially for multi-class problems and problems with unbalanced classes. We also investigate the complexity factors which arise in each type of accuracy-based LCS. We provide a model on the learning complexity of LCS which is based on the representative examples given to the system. The results and observations are also extended to a set of real world classification problems, where accuracy-based LCS are shown to perform competitively with respect to other learning algorithms. The work presents an extended analysis of accuracy-based LCS, gives insight into the understanding of the LCS dynamics, and suggests open issues for further improvement of LCS on classification tasks.