Constructing conjunctions using systematic search on decision trees
References (22)
- et al.
Lookahead feature construction for learning hard concepts
- et al.
ID2-of-3: constructive induction of M-of-N concepts for discriminators in decision trees
An SE-tree based characterisation of the induction problem
Efficiently inducing determinations: a complete and systematic search algorithm that uses optimal pruning
Feature construction: an analytic framework and an application to decision trees
- et al.
Boolean feature discovery in empirical learning
Machine Learning
(1990) Constructing conjunctive tests for decision trees
Systematic search for categorical attribute-value data-driven machine learning
Adaptive decision tree algorithms for learning from examples
- et al.
A scheme for feature construction and a comparison of empirical methods
Constructing new attributes for decision tree learning
Cited by (10)
Mass spectrometry metabolomic data handling for biomarker discovery
2019, Proteomic and Metabolomic Approaches to Biomarker DiscoveryMass Spectrometry Metabolomic Data Handling for Biomarker Discovery
2013, Proteomic and Metabolomic Approaches to Biomarker DiscoveryHybrid decision tree
2002, Knowledge-Based SystemsA new inverse N<sup>th</sup> gravitation based clustering method for data classification
2017, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Automatic Defense Against Zero-day Polymorphic Worms in Communication Networks
2016, Automatic Defense Against Zero-day Polymorphic Worms in Communication NetworksUnsupervised feature construction for improving data representation and semantics
2013, Journal of Intelligent Information Systems
Copyright © 1998 Published by Elsevier B.V.