C4.5: programs for machine learning
C4.5: programs for machine learning
Characterizing the applicability of classification algorithms using meta-level learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Recursive Automatic Bias Selection for Classifier Construction
Machine Learning - Special issue on bias evaluation and selection
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
God Doesn't Always Shave with Occam's Razor - Learning When and How to Prune
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Experiments in Meta-level Learning with ILP
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Discovering Task Neighbourhoods Through Landmark Learning Performances
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Feature Selection for Meta-learning
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
The lack of a priori distinctions between learning algorithms
Neural Computation
The existence of a priori distinctions between learning algorithms
Neural Computation
Layered concept-learning and dynamically variable bias management
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Introduction to the Special Issue on Meta-Learning
Machine Learning
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Meta-learning with Machine Generators and Complexity Controlled Exploration
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Information-Theoretic Measures for Meta-learning
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Learning from the Past with Experiment Databases
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
On the Importance of Comprehensible Classification Models for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Control of complex machines for meta-learning in computational intelligence
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Prediction of classifier training time including parameter optimization
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Experiment databases: a novel methodology for experimental research
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Flexible Algorithm Selection Framework for Large Scale Metalearning
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
Efficient feature size reduction via predictive forward selection
Pattern Recognition
Towards UCI+: A mindful repository design
Information Sciences: an International Journal
Automatic selection of classification learning algorithms for data mining practitioners
Intelligent Data Analysis
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This paper presents new measures, based on the induced decision tree, to characterise datasets for meta-learning in order to select appropriate learning algorithms. The main idea is to capture the characteristics of dataset from the structural shape and size of decision tree induced from the dataset. Totally 15 measures are proposed to describe the structure of a decision tree. Their effectiveness is illustrated through extensive experiments, by comparing to the results obtained by the existing data characteristics techniques, including data characteristics tool (DCT) that is the most wide used technique in meta-learning, and Landmarking that is the most recently developed method.