9 research outputs found
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
Forecasting of residential unit's heat demands: a comparison of machine learning techniques in a real-world case study
A large proportion of the energy consumed by private households is used for space heating and domestic hot water. In the context of the energy transition, the predominant aim is to reduce this consumption. In addition to implementing better energy standards in new buildings and refurbishing old buildings, intelligent energy management concepts can also contribute by operating heat generators according to demand based on an expected heat requirement. This requires forecasting models for heat demand to be as accurate and reliable as possible. In this paper, we present a case study of a newly built medium-sized living quarter in central Europe made up of 66 residential units from which we gathered consumption data for almost two years. Based on this data, we investigate the possibility of forecasting heat demand using a variety of time series models and offline and online machine learning (ML) techniques in a standard data science approach. We chose to analyze different modeling techniques as they can be used in different settings, where time series models require no additional data, offline ML needs a lot of data gathered up front, and online ML could be deployed from day one. A special focus lies on peak demand and outlier forecasting, as well as investigations into seasonal expert models. We also highlight the computational expense and explainability characteristics of the used models. We compare the used methods with naive models as well as each other, finding that time series models, as well as online ML, do not yield promising results. Accordingly, we will deploy one of the offline ML models in our real-world energy management system in the near future
Learning classifier systems from first principles: A probabilistic reformulation of learning classifier systems from the perspective of machine learning
Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being initially designed for such tasks. It is out contention that improvement is hindered by a lack of theoretical understanding of their underlying mechanisms and dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Facing online challenges using learning classifier systems
Els grans avenƧos en el camp de lāaprenentatge automĆ tic han resultat en el disseny de mĆ quines competents que sĆ³n capaces dāaprendre i dāextreure informaciĆ³ Ćŗtil i original de lāexperiĆØncia. Recentment, algunes dāaquestes tĆØcniques dāaprenentatge sāhan aplicat amb ĆØxit per resoldre problemes del mĆ³n real en Ć mbits tecnolĆ²gics, mĆØdics, cientĆfics i industrials, els quals no es podien tractar amb tĆØcniques convencionals dāanĆ lisi ja sigui per la seva complexitat o pel gran volum de dades a processar. Donat aquest ĆØxit inicial, actualment els sistemes dāaprenentatge sāenfronten a problemes de complexitat mĆ©s elevada, el que ha resultat en un augment de lāactivitat investigadora entorn sistemes capaƧos dāafrontar nous problemes del mĆ³n real eficientment i de manera escalable.
Una de les famĆlies dāalgorismes mĆ©s prometedores en lāaprenentatge automĆ tic sĆ³n els sistemes classificadors basats en algorismes genetics (LCSs), el funcionament dels quals sāinspira en la natura. Els LCSs intenten representar les polĆtiques dāactuaciĆ³ dāexperts humans amb un conjunt de regles que sāempren per escollir les millors accions a realitzar en tot moment. AixĆ doncs, aquests sistemes aprenen polĆtiques dāactuaciĆ³ de manera incremental a mida que van adquirint experiĆØncia a travĆ©s de la informaciĆ³ nova que seāls va presentant durant el temps. Els LCSs sāhan aplicat, amb ĆØxit, a camps tan diversos com la predicciĆ³ de cĆ ncer de prĆ²stata o el suport a la inversiĆ³ en borsa, entre altres. A mĆ©s en alguns casos sāha demostrat que els LCSs realitzen tasques superant la precisiĆ³ dels Ć©ssers humans. El propĆ²sit dāaquesta tesi Ć©s explorar la naturalesa de lāaprenentatge online dels LCSs dāestil Michigan per a la mineria de grans quantitats de dades en forma de fluxos dāinformaciĆ³ continus a alta velocitat i canviants en el temps. Molt sovint, lāextracciĆ³ de coneixement a partir dāaquestes fonts de dades Ć©s clau per tal dāobtenir una millor comprensiĆ³ dels processos que les dades estan descrivint. AixĆ, aprendre dāaquestes dades planteja nous reptes a les tĆØcniques tradicionals dāaprenentatge automĆ tic, 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 arbitrĆ ria. La contribuciĆ³ de la present tesi pren lāeXtended Classifier System (XCS), el LCS dāestil Michigan mĆ©s estudiat i un dels algoritmes dāaprenentatge automĆ tic mĆ©s competents, com el punt de partida. Dāaquesta manera els reptes abordats en aquesta tesi sĆ³n dos: el primer desafiament Ć©s la construcciĆ³ dāun sistema supervisat competent sobre el framework dels LCSs dāestil Michigan que aprĆØn dels fluxos de dades amb una capacitat de reacciĆ³ rĆ pida als canvis de concepte i entrades amb soroll. Com moltes aplicacions cientĆfiques i industrials generen grans quantitats de dades sense etiquetar, el segon repte Ć©s aplicar les lliƧons apreses per continuar amb el disseny de LCSs dāestil Michigan capaƧos de solucionar problemes online sense assumir una estructura a priori en els dades dāentrada.Los grandes avances en el campo del aprendizaje automĆ”tico han resultado en el diseƱo de mĆ”quinas capaces de aprender y de extraer informaciĆ³n Ćŗtil y original de la experiencia. Recientemente alguna de estas tĆ©cnicas de aprendizaje se han aplicado con Ć©xito para resolver problemas del mundo real en Ć”mbitos tecnolĆ³gicos, mĆ©dicos, cientĆficos e industriales, los cuales no se podĆan tratar con tĆ©cnicas convencionales de anĆ”lisis ya sea por su complejidad o por el gran volumen de datos a procesar. Dado este Ć©xito inicial, los sistemas de aprendizaje automĆ”tico se enfrentan actualmente a problemas de complejidad cada vez m Ģas 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 mĆ”s prometedoras dentro del aprendizaje automĆ”tico son los sistemas clasificadores basados en algoritmos genĆ©ticos (LCSs), el funcionamiento de los cuales se inspira en la naturaleza. Los LCSs intentan representar las polĆticas de actuaciĆ³n de expertos humanos usando conjuntos de reglas que se emplean para escoger las mejores acciones a realizar en todo momento. AsĆ pues estos sistemas aprenden polĆticas de actuaciĆ³n de manera incremental mientras van adquiriendo experiencia a travĆ©s de la nueva informaciĆ³n que se les va presentando. Los LCSs se han aplicado con Ć©xito en campos tan diversos como en la predicciĆ³n de cĆ”ncer de prĆ³stata o en sistemas de soporte de bolsa, entre otros. AdemĆ”s en algunos casos se ha demostrado que los LCSs realizan tareas superando la precisiĆ³n de expertos humanos.
El propĆ³sito de la presente tesis es explorar la naturaleza online del aprendizaje empleado por los LCSs de estilo Michigan para la minerĆa de grandes cantidades de datos en forma de flujos continuos de informaciĆ³n a alta velocidad y cambiantes en el tiempo. La extracciĆ³n del conocimiento a partir de estas fuentes de datos es clave para obtener una mejor comprensiĆ³n de los procesos que se describen. AsĆ, aprender de estos datos plantea nuevos retos a las tĆ©cnicas tradicionales, las cuales no estĆ”n diseƱadas para tratar flujos de datos continuos y donde los conceptos y los niveles de ruido pueden variar en el tiempo de forma arbitraria. La contribuciĆ³n del la presente tesis toma el eXtended Classifier System (XCS), el LCS de tipo Michigan mĆ”s estudiado y uno de los sistemas de aprendizaje automĆ”tico mĆ”s competentes, como punto de partida. De esta forma los retos abordados en esta tesis son dos: el primer desafĆo es la construcciĆ³n de un sistema supervisado competente sobre el framework de los LCSs de estilo Michigan que aprende de flujos de datos con una capacidad de reacciĆ³n rĆ”pida a los cambios de concepto y al ruido. Como muchas aplicaciones cientĆficas e industriales generan grandes volĆŗmenes de datos sin etiquetar, el segundo reto es aplicar las lecciones aprendidas para continuar con el diseƱo 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-Āāworld 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-Āāworld problems.
One of the most appealing machine learning paradigms are Learning Classifier Systems (LCSs), and more specifically Michigan-Āāstyle 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-Āā 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-Āāstyle LCSs for mining large amounts of data in the form of continuous, high speed and time-Āāchanging 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-Āāstyle 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-Āāstyle 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-Āāstyle LCSs that handle online problems without assuming any a priori structure in input data
Probabilistic prediction of Alzheimerās disease from multimodal image data with Gaussian processes
Alzheimerās disease, the most common form of dementia, is an extremely serious health problem, and one that will become even more so in the coming decades as the global population ages. This has led to a massive effort to develop both new treatments for the condition and new methods of diagnosis; in fact the two are intimately linked as future treatments will depend on earlier diagnosis, which in turn requires the development of biomarkers that can be used to identify and track the disease. This is made possible by studies such as the Alzheimerās disease neuroimaging initiative which provides previously unimaginable quantities of imaging and other data freely to researchers. It is the task of early diagnosis that this thesis focuses on. We do so by borrowing modern machine learning techniques, and applying them to image data. In particular, we use Gaussian processes (GPs), a previously neglected tool, and show they can be used in place of the more widely used support vector machine (SVM). As combinations of complementary biomarkers have been shown to be more useful than the biomarkers are individually, we go on to show GPs can also be applied to integrate different types of image and non-image data, and thanks to their properties this improves results further than it does with SVMs. In the final two chapters, we also look at different ways to formulate both the prediction of conversion to Alzheimerās disease as a machine learning problem and the way image data can be used to generate features for input as a machine learning algorithm. Both of these show how unconventional approaches may improve results. The result is an advance in the state-of-the-art for a very clinically important problem, which may prove useful in practice and show a direction of future research to further increase the usefulness of such method