Ricardo Vilalta

A perspective view and survey of meta-learning

Different researchers hold different views of what the term meta-learning exactlymeans. The first part of this paper provides our own perspective view in which the goal isto build self-adaptive learners (i.e. learning algorithms that improve their bias dynamicallythrough experience by accumulating meta-knowledge). The second part provides a survey ofmeta-learning as reported by the machine-learning literature. We find that, despite differentviews and research lines, a question remains constant: how can we exploit knowledge aboutlearning (i.e. meta-knowledge) to improve the performance of learning algorithms? Clearlythe answer to this question is key to the advancement of the field and continues being thesubject of intensive research.

Predicting rare events in temporal domains

Temporal data mining aims at finding patterns in historical data. Our work proposes an approach to extract temporal patterns from data to predict the occurrence of target events, such as computer attacks on host networks, or fraudulent transactions in financial institutions. Our problem formulation exhibits two major challenges: 1) we assume events being characterized by categorical features and displaying uneven inter-arrival times; such an assumption falls outside the scope of classical time-series analysis, 2) we assume target events are highly infrequent; predictive techniques must deal with the class-imbalance problem. We propose an efficient algorithm that tackles the challenges above by transforming the event prediction problem into a search for all frequent eventsets preceding target events. The class imbalance problem is overcome by a search for patterns on the minority class exclusively; the discrimination power of patterns is then validated against other classes. Patterns are then combined into a rule-based model for prediction. Our experimental analysis indicates the types of event sequences where target events can be accurately predicted.

Introduction to the Special Issue on Meta-Learning

Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.

Predictive algorithms in the management of computer systems

Predictive algorithms play a crucial role in systems management by alerting the user to potential failures. We report on three case studies dealing with the prediction of failures in computer systems: (1) long-term prediction of performance variables (e.g., disk utilization), (2) short-term prediction of abnormal behavior (e.g., threshold violations), and (3) short-term prediction of system events (e.g., router failure). Empirical results show that predictive algorithms can be successfully employed in the estimation of performance variables and the prediction of critical events.

Using Meta-Learning to Support Data Mining

Current data mining tools are characterized by a plethora of algorithms but a lack of guidelines to select the right method according to the nature of the problem under analysis. Producing such guidelines is a primary goal by the field of meta-learning; the research objective is to understand the interaction between the mechanism of learning and the concrete contexts in which that mechanism is applicable. The field of meta-learning has seen continuous growth in the past years with interesting new developments in the construction of practical model-selection assistants, task-adaptive learners, and a solid conceptual framework. In this paper, we give an overview of different techniques necessary to build meta-learning systems. We begin by describing an idealized meta-learning architecture comprising a variety of relevant component techniques. We then look at how each technique has been studied and implemented by previous research. In addition, we show how metalearning has already been identified as an important component in real-world applications.

A decomposition of classes via clustering to explain and improve Naive Bayes

We propose a method to improve the probability estimates made by Naive Bayes to avoid the effects of poor class conditional probabilities based on product distributions when each class spreads into multiple regions. Our approach is based on applying a clustering algorithm to each subset of examples that belong to the same class, and to consider each cluster as a class of its own. Experiments on 26 real-world datasets show a significant improvement in performance when the class decomposition process is applied, particularly when the mean number of clusters per class is large.

Digital topography models for Martian surfaces

We propose to use an unsupervised automated classification of topographic features on Mars in order to speed up geomorphic and geologic mapping of the planet. We construct a digital topography model (DTM), a multilayer grid that stores various kinds of topographical information for every pixel in a site. The method uses a probabilistic clustering algorithm to assign topographically meaningful labels to all pixels in the DTM. The results are displayed as a thematic map of topography. Resultant topographical features are characterized and compared using statistics of their constituent pixels. We demonstrate the usage of our method by classifying and characterizing the topography of a landscape in the Tisia Valles region on Mars. We discuss extensions and further applications of our method.

Using representative-based clustering for nearest neighbor dataset editing

The goal of dataset editing in instance-based learning is to remove objects from a training set in order to increase the accuracy of a classifier. For example, Wilson editing removes training examples that are misclassified by a nearest neighbor classifier so as to smooth the shape of the resulting decision boundaries. This paper revolves around the use of representative-based clustering algorithms for nearest neighbor dataset editing. We term this approach supervised clustering editing. The main idea is to replace a dataset by a set of cluster prototypes. A clustering approach called supervised clustering is introduced for this purpose. Our empirical evaluation using eight UCI datasets shows that both Wilson and supervised clustering editing improve accuracy on more than 50% of the datasets tested. However, supervised clustering editing achieves four times higher compression rates than Wilson editing.

Tablet PC video based hybrid coursework in computer science: report from a pilot project

Online learning, defined broadly as recording and delivering classroom experience with technology, has tremendous potential. However, success to date has been very limited in science and engineering. We believe this is because traditional video recording is cumbersome and not suitable for technical lectures and removing live classroom interaction is detrimental to learning. Employing Tablet PCs with slide presentation software has made it simple and convenient to develop and record high quality lectures. We employ such videos in a hybrid model of coursework. All lectures are made available as online videos, but limited classroom interaction is an important component; the classroom is used for review of lecture material, examinations, demonstrations, tutorials, and hands-on sessions. The hybrid framework is particularly suitable for students with logistical difficulties, e.g., because of work schedule. This paper is an evaluation of the hybrid learning approach as applied to upper level computer science coursework. We report our experience in teaching a suite of hybrid courses at the University of Houston and discuss the detailed feedback we received from the students who participated in the courses.

Meta-Learning - Concepts and Techniques

The field of meta-learning has as one of its primary goals the understanding of the interaction between the mechanism of learning and the concrete contexts in which that mechanism is applicable. The field has seen a continuous growth in the past years with interesting new developments in the construction of practical model-selection assistants, task-adaptive learners, and a solid conceptual framework. In this chapter we give an overview of different techniques necessary to build meta-learning systems. We begin by describing an idealized meta-learning architecture comprising a variety of relevant component techniques. We then look at how each technique has been studied and implemented by previous research. In addition we show how meta-learning has already been identified as an important component in real-world applications.