MILCS: A mutual information learning classifier system

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM

INVESTIGATIONS INTO THE COGNITIVE ABILITIES OF ALTERNATE LEARNING CLASSIFIER SYSTEM ARCHITECTURES

The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for machine learning design and implementation. Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve optimal classifier sets in particular applications requiring rational thought. This research examines LCS and XCS performance in artificial situations with broad social/commercial parallels, created using the non-Markov Iterated Prisoner\u27s Dilemma (IPD) game-playing scenario, where the setting is sometimes asymmetric and where irrationality sometimes pays. This research systematically perturbs a conventional IPD-playing LCS-based agent until it results in a full-fledged XCS-based agent, contrasting the simulated behavior of each LCS variant in terms of a number of performance measures. The intent is to examine the XCS paradigm to understand how it better copes with a given situation (if it does) than the LCS perturbations studied.Experiment results indicate that the majority of the architectural differences do have a significant effect on the agents\u27 performance with respect to the performance measures used in this research. The results of these competitions indicate that while each architectural difference significantly affected its agent\u27s performance, no single architectural difference could be credited as causing XCS\u27s demonstrated superiority in evolving optimal populations. Instead, the data suggests that XCS\u27s ability to evolve optimal populations in the multiplexer and IPD problem domains result from the combined and synergistic effects of multiple architectural differences.In addition, it is demonstrated that XCS is able to reliably evolve the Optimal Population [O] against the TFT opponent. This result supports Kovacs\u27 Optimality Hypothesis in the IPD environment and is significant because it is the first demonstrated occurrence of this ability in an environment other than the multiplexer and Woods problem domains.It is therefore apparent that while XCS performs better than its LCS-based counterparts, its demonstrated superiority may not be attributed to a single architectural characteristic. Instead, XCS\u27s ability to evolve optimal classifier populations in the multiplexer problem domain and in the IPD problem domain studied in this research results from the combined and synergistic effects of multiple architectural differences

Facing online challenges using learning classifier systems

Els grans avenços en el camp de l’aprenentatge automàtic han resultat en el disseny de màquines competents que són capaces d’aprendre i d’extreure informació útil i original de l’experiència. Recentment, algunes d’aquestes tècniques d’aprenentatge s’han aplicat amb èxit per resoldre problemes del món real en àmbits tecnològics, mèdics, científics i industrials, els quals no es podien tractar amb tècniques convencionals d’anàlisi ja sigui per la seva complexitat o pel gran volum de dades a processar. Donat aquest èxit inicial, actualment els sistemes d’aprenentatge s’enfronten a problemes de complexitat més elevada, el que ha resultat en un augment de l’activitat investigadora entorn sistemes capaços d’afrontar nous problemes del món real eficientment i de manera escalable. Una de les famílies d’algorismes més prometedores en l’aprenentatge automàtic són els sistemes classificadors basats en algorismes genetics (LCSs), el funcionament dels quals s’inspira en la natura. Els LCSs intenten representar les polítiques d’actuació d’experts humans amb un conjunt de regles que s’empren per escollir les millors accions a realitzar en tot moment. Així doncs, aquests sistemes aprenen polítiques d’actuació de manera incremental a mida que van adquirint experiència a través de la informació nova que se’ls va presentant durant el temps. Els LCSs s’han aplicat, amb èxit, a camps tan diversos com la predicció de càncer de pròstata o el suport a la inversió en borsa, entre altres. A més en alguns casos s’ha demostrat que els LCSs realitzen tasques superant la precisió dels éssers humans. El propòsit d’aquesta tesi és explorar la naturalesa de l’aprenentatge online dels LCSs d’estil Michigan per a la mineria de grans quantitats de dades en forma de fluxos d’informació continus a alta velocitat i canviants en el temps. Molt sovint, l’extracció de coneixement a partir d’aquestes fonts de dades és clau per tal d’obtenir una millor comprensió dels processos que les dades estan descrivint. Així, aprendre d’aquestes dades planteja nous reptes a les tècniques tradicionals d’aprenentatge automàtic, les quals no estan dissenyades per tractar fluxos de dades continus i on els conceptes i els nivells de soroll poden variar amb el temps de forma arbitrària. La contribució de la present tesi pren l’eXtended Classifier System (XCS), el LCS d’estil Michigan més estudiat i un dels algoritmes d’aprenentatge automàtic més competents, com el punt de partida. D’aquesta manera els reptes abordats en aquesta tesi són dos: el primer desafiament és la construcció d’un sistema supervisat competent sobre el framework dels LCSs d’estil Michigan que aprèn dels fluxos de dades amb una capacitat de reacció ràpida als canvis de concepte i entrades amb soroll. Com moltes aplicacions científiques i industrials generen grans quantitats de dades sense etiquetar, el segon repte és aplicar les lliçons apreses per continuar amb el disseny de LCSs d’estil Michigan capaços de solucionar problemes online sense assumir una estructura a priori en els dades d’entrada.Los grandes avances en el campo del aprendizaje automático han resultado en el diseño de máquinas capaces de aprender y de extraer información útil y original de la experiencia. Recientemente alguna de estas técnicas de aprendizaje se han aplicado con éxito para resolver problemas del mundo real en ámbitos tecnológicos, médicos, científicos e industriales, los cuales no se podían tratar con técnicas convencionales de análisis ya sea por su complejidad o por el gran volumen de datos a procesar. Dado este éxito inicial, los sistemas de aprendizaje automático se enfrentan actualmente a problemas de complejidad cada vez m ́as elevada, lo que ha resultado en un aumento de la actividad investigadora en sistemas capaces de afrontar nuevos problemas del mundo real de manera eficiente y escalable. Una de las familias más prometedoras dentro del aprendizaje automático son los sistemas clasificadores basados en algoritmos genéticos (LCSs), el funcionamiento de los cuales se inspira en la naturaleza. Los LCSs intentan representar las políticas de actuación de expertos humanos usando conjuntos de reglas que se emplean para escoger las mejores acciones a realizar en todo momento. Así pues estos sistemas aprenden políticas de actuación de manera incremental mientras van adquiriendo experiencia a través de la nueva información que se les va presentando. Los LCSs se han aplicado con éxito en campos tan diversos como en la predicción de cáncer de próstata o en sistemas de soporte de bolsa, entre otros. Además en algunos casos se ha demostrado que los LCSs realizan tareas superando la precisión de expertos humanos. El propósito de la presente tesis es explorar la naturaleza online del aprendizaje empleado por los LCSs de estilo Michigan para la minería de grandes cantidades de datos en forma de flujos continuos de información a alta velocidad y cambiantes en el tiempo. La extracción del conocimiento a partir de estas fuentes de datos es clave para obtener una mejor comprensión de los procesos que se describen. Así, aprender de estos datos plantea nuevos retos a las técnicas tradicionales, las cuales no están diseñadas para tratar flujos de datos continuos y donde los conceptos y los niveles de ruido pueden variar en el tiempo de forma arbitraria. La contribución del la presente tesis toma el eXtended Classifier System (XCS), el LCS de tipo Michigan más estudiado y uno de los sistemas de aprendizaje automático más competentes, como punto de partida. De esta forma los retos abordados en esta tesis son dos: el primer desafío es la construcción de un sistema supervisado competente sobre el framework de los LCSs de estilo Michigan que aprende de flujos de datos con una capacidad de reacción rápida a los cambios de concepto y al ruido. Como muchas aplicaciones científicas e industriales generan grandes volúmenes de datos sin etiquetar, el segundo reto es aplicar las lecciones aprendidas para continuar con el diseño de nuevos LCSs de tipo Michigan capaces de solucionar problemas online sin asumir una estructura a priori en los datos de entrada.Last advances in machine learning have fostered the design of competent algorithms that are able to learn and extract novel and useful information from data. Recently, some of these techniques have been successfully applied to solve real-­‐world problems in distinct technological, scientific and industrial areas; problems that were not possible to handle by the traditional engineering methodology of analysis either for their inherent complexity or by the huge volumes of data involved. Due to the initial success of these pioneers, current machine learning systems are facing problems with higher difficulties that hamper the learning process of such algorithms, promoting the interest of practitioners for designing systems that are able to scalably and efficiently tackle real-­‐world problems. One of the most appealing machine learning paradigms are Learning Classifier Systems (LCSs), and more specifically Michigan-­‐style LCSs, an open framework that combines an apportionment of credit mechanism with a knowledge discovery technique inspired by biological processes to evolve their internal knowledge. In this regard, LCSs mimic human experts by making use of rule lists to choose the best action to a given problem situation, acquiring their knowledge through the experience. LCSs have been applied with relative success to a wide set of real-­‐ world problems such as cancer prediction or business support systems, among many others. Furthermore, on some of these areas LCSs have demonstrated learning capacities that exceed those of human experts for that particular task. The purpose of this thesis is to explore the online learning nature of Michigan-­‐style LCSs for mining large amounts of data in the form of continuous, high speed and time-­‐changing streams of information. Most often, extracting knowledge from these data is key, in order to gain a better understanding of the processes that the data are describing. Learning from these data poses new challenges to traditional machine learning techniques, which are not typically designed to deal with data in which concepts and noise levels may vary over time. The contribution of this thesis takes the extended classifier system (XCS), the most studied Michigan-­‐style LCS and one of the most competent machine learning algorithms, as the starting point. Thus, the challenges addressed in this thesis are twofold: the first challenge is building a competent supervised system based on the guidance of Michigan-­‐style LCSs that learns from data streams with a fast reaction capacity to changes in concept and noisy inputs. As many scientific and industrial applications generate vast amounts of unlabelled data, the second challenge is to apply the lessons learned in the previous issue to continue with the design of unsupervised Michigan-­‐style LCSs that handle online problems without assuming any a priori structure in input data

学習戦略に基づく学習分類子システムの設計

On Learning Classifier Systems dubbed LCSs a leaning strategy which defines how LCSs cover a state-action space in a problem can be one of the most fundamental options in designing LCSs. There lacks an intensive study of the learning strategy to understand whether and how the learning strategy affects the performance of LCSs. This lack has resulted in the current design methodology of LCS which does not carefully consider the types of learning strategy. The thesis clarifies a need of a design methodology of LCS based on the learning strategy. That is, the thesis shows the learning strategy can be an option that determines the potential performance of LCSs and then claims that LCSs should be designed on the basis of the learning strategy in order to improve the performance of LCSs. First, the thesis empirically claims that the current design methodology of LCS, without the consideration of learning strategy, can be limited to design a proper LCS to solve a problem. This supports the need of design methodology based on the learning strategy. Next, the thesis presents an example of how LCS can be designed on the basis of the learning strategy. The thesis empirically show an adequate learning strategy improving the performance of LCS can be decided depending on a type of problem difficulties such as missing attributes. Then, the thesis draws an inclusive guideline that explains which learning strategy should be used to address which types of problem difficulties. Finally, the thesis further shows, on an application of LCS for a human daily activity recognition problem, the adequate learning strategy according to the guideline effectively improves the performance of the application. The thesis concludes that the learning strategy is the option of the LCS design which determines the potential performance of LCSs. Thus, before designing any type of LCSs including their applications, the learning strategy should be adequately selected at first, because their performance degrades when they employ an inadequate learning strategy to a problem they want to solve. In other words, LCSs should be designed on the basis of the adequate learning strategy.電気通信大学201

Evolutionary Strategies for Data Mining

Learning classifier systems (LCS) have been successful in generating rules for solving classification problems in data mining. The rules are of the form IF condition THEN action. The condition encodes the features of the input space and the action encodes the class label. What is lacking in those systems is the ability to express each feature using a function that is appropriate for that feature. The genetic algorithm is capable of doing this but cannot because only one type of membership function is provided. Thus, the genetic algorithm learns only the shape and placement of the membership function, and in some cases, the number of partitions generated by this function. The research conducted in this study employs a learning classifier system to generate the rules for solving classification problems, but also incorporates multiple types of membership functions, allowing the genetic algorithm to choose an appropriate one for each feature of the input space and determine the number of partitions generated by each function. In addition, three membership functions were introduced. This paper describes the framework and implementation of this modified learning classifier system (M-LCS). Using the M-LCS model, classifiers were simulated for two benchmark classification problems and two additional real-world problems. The results of these four simulations indicate that the M-LCS model provides an alternative approach to designing a learning classifier system. The following contributions are made to the field of computing: 1) a framework for developing a learning classifier system that employs multiple types of membership functions, 2) a model, M-LCS, that was developed from the framework, and 3) the addition of three membership functions that have not been used in the design of learning classifier systems

Contributions to comprehensible classification

xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID

Intelligent network intrusion detection using an evolutionary computation approach

With the enormous growth of users\u27 reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems. Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely remove the threat of network attacks. A good security mechanism requires an Intrusion Detection System (IDS) in order to monitor security breaches when the prevention schemes in the preparation phase are bypassed. To be able to react to network attacks as fast as possible, an automatic detection system is of paramount importance. The later an attack is detected, the less time network administrators have to update their signatures and reconfigure their detection and remediation systems. An IDS is a tool for monitoring the system with the aim of detecting and alerting intrusive activities in networks. These tools are classified into two major categories of signature-based and anomaly-based. A signature-based IDS stores the signature of known attacks in a database and discovers occurrences of attacks by monitoring and comparing each communication in the network against the database of signatures. On the other hand, mechanisms that deploy anomaly detection have a model of normal behaviour of system and any significant deviation from this model is reported as anomaly. This thesis aims at addressing the major issues in the process of developing signature based IDSs. These are: i) their dependency on experts to create signatures, ii) the complexity of their models, iii) the inflexibility of their models, and iv) their inability to adapt to the changes in the real environment and detect new attacks. To meet the requirements of a good IDS, computational intelligence methods have attracted considerable interest from the research community. This thesis explores a solution to automatically generate compact rulesets for network intrusion detection utilising evolutionary computation techniques. The proposed framework is called ESR-NID (Evolving Statistical Rulesets for Network Intrusion Detection). Using an interval-based structure, this method can be deployed for any continuous-valued input data. Therefore, by choosing appropriate statistical measures (i.e. continuous-valued features) of network trafc as the input to ESRNID, it can effectively detect varied types of attacks since it is not dependent on the signatures of network packets. In ESR-NID, several innovations in the genetic algorithm were developed to keep the ruleset small. A two-stage evaluation component in the evolutionary process takes the cooperation of rules into consideration and results into very compact, easily understood rulesets. The effectiveness of this approach is evaluated against several sources of data for both detection of normal and abnormal behaviour. The results are found to be comparable to those achieved using other machine learning methods from both categories of GA-based and non-GA-based methods. One of the significant advantages of ESR-NIS is that it can be tailored to specific problem domains and the characteristics of the dataset by the use of different fitness and performance functions. This makes the system a more flexible model compared to other learning techniques. Additionally, an IDS must adapt itself to the changing environment with the least amount of configurations. ESR-NID uses an incremental learning approach as new flow of traffic become available. The incremental learning approach benefits from less required storage because it only keeps the generated rules in its database. This is in contrast to the infinitely growing size of repository of raw training data required for traditional learning

Contributions to comprehensible classification

xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID