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Ontology-based meta-mining of knowledge discovery workflows
- In: Meta-Learning in Computational Intelligence; vol. 358 of Studies in Computational Intelligence
"... This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred t ..."
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This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred to as meta-mining. Second, traditional meta-learning regards learning algorithms as black boxes and essentially correlates properties of their input (data) with the performance of their output (learned model). We propose to tear open the black box and anal-yse algorithms in terms of their core components, their underlying assumptions, the cost functions and optimization strategies they use, and the models and de-cision boundaries they generate. Third, to ground meta-mining on a declarative representation of the data mining (dm) process and its components, we built a DM ontology and knowledge base using the Web Ontology Language (owl). The Data Mining Optimization Ontology (dmop, pronounced dee-mope)) pro-vides a unified conceptual framework for analysing dm tasks, algorithms, models,
T.B.: Active learning to support the generation of meta-examples
- In: Proc. of the International Conference on Artificial Neural Networks. (2007) 817–826
"... Abstract. Meta-Learning has been used to select algorithms based on the features of the problems being tackled. Each training example in this context, i.e. each meta-example, stores the features of a given problem and the performance information obtained by the candidate algorithms in the problem. T ..."
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Abstract. Meta-Learning has been used to select algorithms based on the features of the problems being tackled. Each training example in this context, i.e. each meta-example, stores the features of a given problem and the performance information obtained by the candidate algorithms in the problem. The construction of a set of meta-examples may be costly, since the algorithms performance is usually defined through an empirical evaluation on the problem at hand. In this context, we proposed the use of Active Learning to select only the relevant problems for meta-example generation. Hence, the need for empirical evaluations of the candidate algorithms is reduced. Experiments were performed using the classification uncertainty of the k-NN algorithm as the criteria for active selection of problems. A significant gain in performance was yielded by using the Active Learning method. 1
Ranking and Selecting Clustering Algorithms Using a Meta-Learning Approach
, 2008
"... We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algori ..."
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Cited by 5 (2 self)
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We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs regression support vector machines as the meta-learner. Our case study is developed in the context of cancer gene expression microarray datasets.
Prototypes based relational learning
- In The 13th International Conference on Artificial Intelligence: Methodology, Systems, Applications
, 2008
"... Abstract. Relational instance-based learning (RIBL) algorithms offer high prediction capabilities. However, they do not scale up well, specially in domains where there is a time bound for classification. Nearest prototype approaches can alleviate this problem, by summarizing the data set in a reduce ..."
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Cited by 2 (2 self)
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Abstract. Relational instance-based learning (RIBL) algorithms offer high prediction capabilities. However, they do not scale up well, specially in domains where there is a time bound for classification. Nearest prototype approaches can alleviate this problem, by summarizing the data set in a reduced set of prototypes. In this paper we present an algorithm to build Relational Nearest Prototype Classifiers (rnpc). When compared with RIBL approaches, the algorithm is able to dramatically reduce the number of instances by selecting the most relevant prototypes, maintaining similar accuracy. The number of prototypes is obtained automatically by the algorithm, although it can be also bounded by the user. Empirical results on benchmark data sets demonstrate the utility of this approach compared to other instance based approaches.
Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach
- IN: META-LEARNING IN COMPUTATIONAL INTELLIGENCE. N, JANKOWSKI ET AL. (EDS.). STUDIES IN COMPUTATIONAL INTELLIGENCE 358, 1ST EDITION
, 2011
"... In this work, we present the use of Ranking Meta-Learning approaches to ranking and selecting algorithms for problems of time series forecasting and clustering of gene expression data. Given a problem (forecasting or clustering), the Meta-Learning approach provides a ranking of the candidate algor ..."
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In this work, we present the use of Ranking Meta-Learning approaches to ranking and selecting algorithms for problems of time series forecasting and clustering of gene expression data. Given a problem (forecasting or clustering), the Meta-Learning approach provides a ranking of the candidate algorithms, according to the characteristics of the problem’s dataset. The best ranked algorithm can be returned as the selected one. In order to evaluate the Ranking Meta-Learning proposal, prototypes were implemented to rank artificial neural networks models for forecasting financial and economic time series and to rank clustering algorithms in the context of cancer gene expression microarray datasets. The case studies regard experiments to measure the correlation between the suggested rankings of algorithms and the ideal rankings. The results revealed that Meta-Learning was able to suggest more adequate rankings in both domains of application considered.
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-Regressors
"... Abstract. In this work, we proposed the use of Support Vector Ma-chines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to pre-dict learning performan ..."
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Abstract. In this work, we proposed the use of Support Vector Ma-chines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to pre-dict learning performance, supporting algorithm selection. Experiments were performed in a case study in which SVMs with different kernel functions were used to predict the performance of Multi-Layer Percep-tron (MLP) networks. The SVMs obtained better results in the evaluated task, when compared to different algorithms that have been applied as meta-regressors in previous work. 1
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"... Selective generation of training examples in active meta-learning Article in International journal of hybrid intelligent systems · July 2008 ..."
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Selective generation of training examples in active meta-learning Article in International journal of hybrid intelligent systems · July 2008
Aprendizagem Ativa para Seleção de Exemplos em Meta-Aprendizado
"... Abstract. Meta-Learning has been used to select algorithms based on the features of the problems in which the algorithms can be applied. Each metaexample stores the features of a given problem and the performance information related to the candidate algorithms in the problem. The construction of a s ..."
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Abstract. Meta-Learning has been used to select algorithms based on the features of the problems in which the algorithms can be applied. Each metaexample stores the features of a given problem and the performance information related to the candidate algorithms in the problem. The construction of a set of meta-examples may be costly, since the algorithm performance is usually defined through an empirical evaluation on the problem at hand. In this context, we proposed the use of Active Learning to select only the relevant metaexamples and hence, to reduce the need for empirical evaluations of the candidate algorithms. Experiments were performed using the kNN algorithm was as meta-learner and an uncertainty criteria was applied to select meta-examples. A significant gain in performance was yielded by selecting about 6 % of the available meta-examples. Resumo. Meta-Aprendizado tem sido usado com sucesso para selecionar algoritmos a partir das características dos problemas em que podem ser aplicados. Cada meta-exemplo armazena as características de um dado problema e as
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-Regressors Blind Review
"... In this work, we proposed the use of Support Vector Machines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to predict learning performance, supporti ..."
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In this work, we proposed the use of Support Vector Machines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the Meta-Regression approach, which has been successfully applied to predict learning performance, supporting algorithm selection. Experiments were performed in a case study in which SVMs with different kernel functions were used to predict the performance of Multi-Layer Perceptron (MLP) networks. The SVMs obtained better results in the evaluated task, when compared to different algorithms that have been applied as metaregressors in previous work. 1
