Negative Selection for Algorithm for Anomaly Detection

May 4, 20096 likes4,849 views

Xavier Llorà

Dipankar Dasgupta reviews the negative selection algorithm and its connections to learning classifier systems

Negative Selection for Algorithm for Anomaly Detection

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More from Xavier Llorà (20)

Meandre 2.0 Alpha PreviewXavier Llorà

Soaring the Clouds with MeandreXavier Llorà

This document discusses cloud computing and the Meandre framework. It provides an overview of cloud concepts like public/private clouds and IaaS, PaaS, SaaS models. It describes NCSA's use of virtual machines and Eucalyptus cloud. Meandre is presented as a component-based framework that can orchestrate data-intensive applications across cloud resources through its dataflow model and scripting language. It aims to facilitate scaling applications to leverage elastic cloud infrastructure and integrate computation and data.

From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0Xavier Llorà

One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.

Large Scale Data Mining using Genetics-Based Machine LearningXavier Llorà

We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.

Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...Xavier Llorà

Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.

Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new TrendsXavier Llorà

The document summarizes a presentation given by Jorge Casillas on research related to scaling up genetic learning algorithms and fuzzy classifier systems. Specifically, it discusses: 1. An approach using evolutionary instance selection and stratification to extract rule sets from large datasets that balance prediction accuracy and interpretability. 2. Fuzzy-XCS, an accuracy-based genetic fuzzy system the author is developing that uses competitive fuzzy inference and represents rules as disjunctive normal forms to address challenges in credit assignment. 3. Open problems and opportunities in applying genetic learning at large scales, such as addressing chromosome size and efficient evaluation over large datasets.

Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...Xavier Llorà

This document summarizes research using a Pittsburgh Learning Classifier System (LCS) called GAssist to predict protein structure by determining coordination numbers (CN). The researchers tested GAssist on a dataset of over 250,000 protein residues, comparing it to support vector machines, Naive Bayes, and C4.5 decision trees. While support vector machines achieved the best accuracy, GAssist produced more interpretable and compact rule sets at the cost of lower performance. The researchers analyzed the interpretability and scalability of GAssist for this challenging bioinformatics problem, identifying avenues for improving its accuracy while maintaining explanatory power.

Towards a Theoretical Towards a Theoretical Framework for LCS Framework fo...Xavier Llorà

Learning Classifier Systems for Class Imbalance ProblemsXavier Llorà

The document discusses learning classifier systems (LCS) for addressing class imbalance problems in datasets. It aims to enhance the applicability of LCS to knowledge discovery from real-world datasets that often exhibit class imbalance, where one class is represented by significantly fewer examples than other classes. The author proposes adapting parameters of the XCS learning classifier system, such as learning rate and genetic algorithm threshold, based on estimated class imbalance ratios within classifiers' niches in order to minimize bias towards majority classes and better handle small disjuncts representing minority classes.

A Retrospective Look at A Retrospective Look at Classiﬁer System ResearchCl...Xavier Llorà

XCS: Current capabilities and future challengesXavier Llorà

The document discusses the XCS classifier system, which uses a combination of gradient-based techniques and evolutionary algorithms to learn predictive models from complex problems. It summarizes XCS's current capabilities in classification, function approximation, and reinforcement learning tasks. However, it notes there are still challenges to improve XCS's representations and operators, niching abilities, handling of dynamic problems, solution compactness, and development of hierarchical classifier systems.

Searle, Intentionality, and the Future of Classifier SystemsXavier Llorà

Computed Prediction: So far, so good. What now?Xavier Llorà

This document discusses computed prediction in learning classifier systems (LCS). It addresses representing the payoff function Q(s,a) that maps state-action pairs to expected future payoffs. Specifically: 1) In computed prediction, each classifier has parameters w and the classifier prediction is computed as a parametrized function p(x,w) like a linear approximation. 2) Classifier weights are updated using the Widrow-Hoff rule online as the payoff function is learned. 3) Using a powerful approximator like tile coding to compute predictions allows the problem to potentially be solved by a single classifier, but evolution of different approximators per problem subspace may still

NIGEL 2006 welcomeXavier Llorà

This document provides information about the NCSA/IlliGAL Gathering on Evolutionary Learning (NIGEL 2006) conference. It discusses how the conference originated from a previous 2003 gathering. It thanks the organizers and participants and provides details about the agenda, which includes presentations on topics like classifier systems and discussions around applications and techniques of evolutionary learning.

Linkage Learning for Pittsburgh LCS: Making Problems TractableXavier Llorà

Meandre: Semantic-Driven Data-Intensive Flows in the CloudsXavier Llorà

- Meandre is a semantic-driven data-intensive workflow infrastructure for distributed computing. It allows users to assemble modular components into complex workflows (flows) in a visual programming tool or using a scripting language called ZigZag. - Workflows are composed of components, which can be executable or control components. Executable components perform computational tasks when data is available, while control components pause workflows for user interactions. Components are described semantically using ontologies to separate functionality from implementation. - Data availability drives workflow execution in Meandre. When required inputs are available, components will fire and produce outputs to make data available for downstream components. This dataflow approach aims to make workflows transparent, intuitive, and reusable across

ZigZag: The Meandring LanguageXavier Llorà

HUMIES 2007 Bronze Winner: Towards Better than Human Capability in Diagnosing...Xavier Llorà

Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...Xavier Llorà

A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.

Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...Xavier Llorà

Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.