Randomization tests
Information Processing Letters
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Inferring decision trees using the minimum description length principle
Information and Computation
Learning from hints in neural networks
Journal of Complexity
Elements of information theory
Elements of information theory
Neural networks and the bias/variance dilemma
Neural Computation
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Induction with randomization testing: decision-oriented analysis of large data sets
Induction with randomization testing: decision-oriented analysis of large data sets
Theory refinement combining analytical and empirical methods
Artificial Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Advances in knowledge discovery and data mining
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Multiple Comparisons in Induction Algorithms
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Characterizing the generalization performance of model selection strategies
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Process-Oriented Heuristic for Model Selection
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Process-Oriented Estimation of Generalization Error
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Option Decision Trees with Majority Votes
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
The lack of a priori distinctions between learning algorithms
Neural Computation
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Further experimental evidence against the utility of Occam's razor
Journal of Artificial Intelligence Research
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Lessons in neural network training: overfitting may be harder than expected
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A new metric-based approach to model selection
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery with second-order relations
Knowledge and Information Systems
Beyond Occam's Razor: Process-Oriented Evaluation
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Phase Transitions and Stochastic Local Search in k-Term DNF Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Possibilistic Induction in Decision-Tree Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
The Role of Domain Knowledge in a Large Scale Data Mining Project
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Learning of Boolean Functions Using Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Defining Similarity Measures: Top-Down vs. Bottom-Up
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Data mining tasks and methods: scalability
Handbook of data mining and knowledge discovery
Knowledge evaluation: Other evaluations: minimum description length
Handbook of data mining and knowledge discovery
An empirical comparison of supervised machine learning techniques in bioinformatics
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Data Mining for Generating Predictive Models of Local Hydrology
Applied Intelligence
Machine Learning for Computer Graphics: A Manifesto and Tutorial
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
ART: A Hybrid Classification Model
Machine Learning
Pareto-optimal patterns in logical analysis of data
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Randomised restarted search in ILP
Machine Learning
Design and evaluation of visualization support to facilitate decision trees classification
International Journal of Human-Computer Studies
A Dichotomic Search Algorithm for Mining and Learning in Domain-Specific Logics
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Take a load off: cognitive considerations for game design
Proceedings of the 3rd Australasian conference on Interactive entertainment
Argument based machine learning
Artificial Intelligence
Hybrid systems of local basis functions
Intelligent Data Analysis
Rule effectiveness in rule-based systems: A credit scoring case study
Expert Systems with Applications: An International Journal
CiE '07 Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using crossover based similarity measure to improve genetic programming generalization ability
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Any time induction of decision trees: an iterative improvement approach
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning from ambiguously labeled examples
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Occam's razor just got sharper
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Pareto-optimal patterns in logical analysis of data
Discrete Applied Mathematics
XKey: A tool for the generation of identification keys
Expert Systems with Applications: An International Journal
Interactive visual decision tree classification
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction platforms and techniques
Analysis on classification performance of rough set based reducts
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
An effective sampling scheme for better multi-layer perceptrons
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Evaluating learning algorithms and classifiers
International Journal of Intelligent Information and Database Systems
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
Measuring bloat, overfitting and functional complexity in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Investigating better multi-layer perceptrons for the task of classification
WSEAS Transactions on Computers
A Randomized Exhaustive Propositionalization Approach for Molecule Classification
INFORMS Journal on Computing
Genetic programming, validation sets, and parsimony pressure
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Generality is predictive of prediction accuracy
Data Mining
Learning from ambiguously labeled examples
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Sample complexity of linear learning machines with different restrictions over weights
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Data-Mining-Driven Neighborhood Search
INFORMS Journal on Computing
Performance of classification models from a user perspective
Decision Support Systems
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
A few useful things to know about machine learning
Communications of the ACM
A Dichotomic Search Algorithm for Mining and Learning in Domain-Specific Logics
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
| Hi-index | 0.02 |
Many KDD systems incorporate an implicit or explicitpreference for simpler models, but this use of “Occam‘s razor” hasbeen strongly criticized by several authors (e.g., Schaffer, 1993;Webb, 1996). This controversy arises partly because Occam‘s razor hasbeen interpreted in two quite different ways. The firstinterpretation (simplicity is a goal in itself) is essentiallycorrect, but is at heart a preference for more comprehensible models.The second interpretation (simplicity leads to greater accuracy) ismuch more problematic. A critical review of the theoretical argumentsfor and against it shows that it is unfounded as a universalprinciple, and demonstrably false. A review of empirical evidenceshows that it also fails as a practical heuristic. This articleargues that its continued use in KDD risks causing significantopportunities to be missed, and should therefore be restricted to thecomparatively few applications where it is appropriate. The articleproposes and reviews the use of domain constraints as an alternativefor avoiding overfitting, and examines possible methods for handlingthe accuracy–comprehensibility trade-off.