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A perspective view and survey of meta-learning
- Artificial Intelligence Review
, 2002
"... Abstract. Different researchers hold different views of what the term meta-learning exactly means. The first part of this paper provides our own perspective view in which the goal is to build self-adaptive learners (i.e. learning algorithms that improve their bias dynamically through experience by a ..."
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Cited by 117 (3 self)
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Abstract. Different researchers hold different views of what the term meta-learning exactly means. The first part of this paper provides our own perspective view in which the goal is to build self-adaptive learners (i.e. learning algorithms that improve their bias dynamically through experience by accumulating meta-knowledge). The second part provides a survey of meta-learning as reported by the machine-learning literature. We find that, despite different views and research lines, a question remains constant: how can we exploit knowledge about learning (i.e. meta-knowledge) to improve the performance of learning algorithms? Clearly the answer to this question is key to the advancement of the field and continues being the subject of intensive research.
J.P.: Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results
- Machine Learning
, 2003
"... Abstract. We present a meta-learning method to support selection of candidate learning algorithms. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The distance between datasets is assessed using a relatively small set of data characteristics, ..."
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Cited by 70 (9 self)
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Abstract. We present a meta-learning method to support selection of candidate learning algorithms. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The distance between datasets is assessed using a relatively small set of data characteristics, which was selected to represent properties that affect algorithm performance. The performance of the candidate algorithms on those datasets is used to generate a recommendation to the user in the form of a ranking. The performance is assessed using a multicriteria evaluation measure that takes not only accuracy, but also time into account. As it is not common in Machine Learning to work with rankings, we had to identify and adapt existing statistical techniques to devise an appropriate evaluation methodology. Using that methodology, we show that the meta-learning method presented leads to significantly better rankings than the baseline ranking method. The evaluation methodology is general and can be adapted to other ranking problems. Although here we have concentrated on ranking classification algorithms, the meta-learning framework presented can provide assistance in the selection of combinations of methods or more complex problem solving strategies.
Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification
- IEEE Transactions on Knowledge and Data Engineering
, 2005
"... For more information, please visit our website at ..."
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Active Learning with Multiple Views
, 2002
"... Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which i ..."
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Cited by 54 (1 self)
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Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing. 1.
On data and algorithms: understanding inductive performance
- Machine Learning
, 2004
"... Abstract. In this paper we address two symmetrical issues, the discov-ery of similarities among classification algorithms, and among datasets. Both on the basis of error measures, which we use to define the error cor-relation between two algorithms, and determine the relative performance of a list o ..."
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Cited by 29 (4 self)
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Abstract. In this paper we address two symmetrical issues, the discov-ery of similarities among classification algorithms, and among datasets. Both on the basis of error measures, which we use to define the error cor-relation between two algorithms, and determine the relative performance of a list of algorithms. We use the first to discover similarities between learners, and both of them to discover similarities between datasets. The latter sketch maps on the dataset space. Regions within each map exhibit specific patterns of error correlation or relative performance. To acquire an understanding of the factors determining these regions we describe them using simple characteristics of the datasets. Descriptions of each region are given in terms of the distributions of dataset characteristics within it. 1
A Comparison of Ranking Methods for Classification Algorithm Selection
- In Proceedings of the European Conference on Machine Learning ECML2000 (to Be Published
, 2000
"... . We investigate the problem of using past performance information to select an algorithm for a given classification problem. We present three ranking methods for that purpose: average ranks, success rate ratios and significant wins. We also analyze the problem of evaluating and comparing these ..."
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Cited by 26 (8 self)
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. We investigate the problem of using past performance information to select an algorithm for a given classification problem. We present three ranking methods for that purpose: average ranks, success rate ratios and significant wins. We also analyze the problem of evaluating and comparing these methods. The evaluation technique used is based on a leave-one-out procedure. On each iteration, the method generates a ranking using the results obtained by the algorithms on the training datasets. This ranking is then evaluated by calculating its distance from the ideal ranking built using the performance information on the test dataset. The distance measure adopted here, average correlation, is based on Spearman's rank correlation coefficient. To compare ranking methods, a combination of Friedman's test and Dunn's multiple comparison procedure is adopted. When applied to the methods presented here, these tests indicate that the success rate ratios and average ranks methods perfo...
An Intelligent Assistant for the Knowledge Discovery Process
, 2001
"... A knowledge discovery (KD) process involves preprocessing data, choosing a data-mining algorithm, and postprocessing the mining results. There are very many choices for each of these stages, and non-trivial interactions between them. Consequently, both novices and data-mining specialists need a ..."
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Cited by 22 (0 self)
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A knowledge discovery (KD) process involves preprocessing data, choosing a data-mining algorithm, and postprocessing the mining results. There are very many choices for each of these stages, and non-trivial interactions between them. Consequently, both novices and data-mining specialists need assistance in navigating the space of possible KD processes. We present the concept of Intelligent Discovery Assistants (IDAs), which provide users with (i) systematic enumerations of valid KD processes, so important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of KD processes to execute. We use a prototype to show that an IDA can indeed provide useful enumerations and effective rankings.
Knowledge-Based Visualization to Support Spatial Data Mining
- In Proceedings of the 3rd Symposium on Intelligent Data Analysis
, 1999
"... . Data mining methods are designed for revealing significant relationships and regularities in data collections. Regarding spatially referenced data, analysis by means of data mining can be aptly complemented by visual exploration of the data presented on maps as well as by cartographic visualiz ..."
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Cited by 21 (9 self)
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. Data mining methods are designed for revealing significant relationships and regularities in data collections. Regarding spatially referenced data, analysis by means of data mining can be aptly complemented by visual exploration of the data presented on maps as well as by cartographic visualization of results of data mining procedures. We propose an integrated environment for exploratory analysis of spatial data that equips an analyst with a variety of data mining tools and provides the service of automated mapping of source data and data mining results. The environment is built on the basis of two existing systems, Kepler for data mining and Descartes for automated knowledge-based visualization. It is important that the open architecture of Kepler allows to incorporate new data mining tools, and the knowledge-based architecture of Descartes allows to automatically select appropriate presentation methods according to characteristics of data mining results. The paper pre...
Theoretical Comparison between the Gini Index and Information Gain Criteria
- Annals of Mathematics and Artificial Intelligence
, 2000
"... Knowledge Discovery in Databases (KDD) is an active and important research area with the promise for a high payoff in many business and scientific applications. One of the main tasks in KDD is classification. A particular efficient method for classification is decision tree induction. The selectio ..."
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Cited by 20 (0 self)
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Knowledge Discovery in Databases (KDD) is an active and important research area with the promise for a high payoff in many business and scientific applications. One of the main tasks in KDD is classification. A particular efficient method for classification is decision tree induction. The selection of the attribute used at each node of the tree to split the data (split criterion) is crucial in order to correctly classify objects. Different split criteria were proposed in the literature (Information Gain, Gini Index, etc.). It is not obvious which of them will produce the best decision tree for a given data set. A large amount of empirical tests were conducted in order to answer this question. No conclusive results were found.
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 in ..."
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Cited by 19 (0 self)
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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. 1
