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ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Neural networks and the bias/variance dilemma
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
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Machine Learning - Special issue on bias evaluation and selection
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Data mining: practical machine learning tools and techniques with Java implementations
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Using Model Trees for Classification
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Machine Learning
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Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Combining Classifiers with Meta Decision Trees
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Functional Models for Regression Tree Leaves
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Generating neural networks through the induction of threshold logic unit trees
INBS '95 Proceedings of the First International Symposium on Intelligence in Neural and Biological Systems (INBS'95)
Linear Machine Decision Trees
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
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Incremental Learning of Linear Model Trees
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Intelligent Data Analysis
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Intelligent Data Analysis - Knowledge Discovery from Data Streams
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ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
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ACM SIGKDD Explorations Newsletter
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ISB '10 Proceedings of the International Symposium on Biocomputing
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IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Example-dependent basis vector selection for kernel-based classifiers
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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Information Sciences: an International Journal
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ECML'05 Proceedings of the 16th European conference on Machine Learning
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
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MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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Computer Methods and Programs in Biomedicine
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HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Model selection in omnivariate decision trees using Structural Risk Minimization
Information Sciences: an International Journal
Advanced Engineering Informatics
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International Journal of Data Mining and Bioinformatics
Decision trees: a recent overview
Artificial Intelligence Review
Computer Methods and Programs in Biomedicine
Learning from data streams with only positive and unlabeled data
Journal of Intelligent Information Systems
Application of data mining techniques on EMG registers of hemiplegic patients
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Identifying risky environments for COPD patients using smartphones and internet of things objects
International Journal of Computational Intelligence Studies
A hybrid decision tree classifier
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In the context of classification problems, algorithms that generate multivariate trees are able to explore multiple representation languages by using decision tests based on a combination of attributes. In the regression setting, model trees algorithms explore multiple representation languages but using linear models at leaf nodes. In this work we study the effects of using combinations of attributes at decision nodes, leaf nodes, or both nodes and leaves in regression and classification tree learning. In order to study the use of functional nodes at different places and for different types of modeling, we introduce a simple unifying framework for multivariate tree learning. This framework combines a univariate decision tree with a linear function by means of constructive induction. Decision trees derived from the framework are able to use decision nodes with multivariate tests, and leaf nodes that make predictions using linear functions. Multivariate decision nodes are built when growing the tree, while functional leaves are built when pruning the tree. We experimentally evaluate a univariate tree, a multivariate tree using linear combinations at inner and leaf nodes, and two simplified versions restricting linear combinations to inner nodes and leaves. The experimental evaluation shows that all functional trees variants exhibit similar performance, with advantages in different datasets. In this study there is a marginal advantage of the full model. These results lead us to study the role of functional leaves and nodes. We use the bias-variance decomposition of the error, cluster analysis, and learning curves as tools for analysis. We observe that in the datasets under study and for classification and regression, the use of multivariate decision nodes has more impact in the bias component of the error, while the use of multivariate decision leaves has more impact in the variance component.