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The use of background knowledge in decision tree induction

https://doi.org/10.1007/bf00114778
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-662-12405-5openalex-snapshot
Machine Learning
inferredcites →https://doi.org/10.1016/b978-1-55860-036-2.50099-0openalex-snapshot
Cost-Sensitive Concept Learning of Sensor Use in Approach and Recognition
inferredcites →https://doi.org/10.1109/robot.1990.126097openalex-snapshot
CSL: a cost-sensitive learning system for sensing and grasping objects
inferredcites →https://doi.org/10.1145/1045343.1045369openalex-snapshot
Inductive inference of VL decision rules
inferredcites →https://doi.org/10.2307/1421207openalex-snapshot
Experiments in Induction

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inferred← citeshttps://doi.org/10.1002/9781119175391.ch18openalex-snapshot
Decision Trees
inferred← citeshttps://doi.org/10.1002/nav.20154openalex-snapshot
A misclassification cost‐minimizing evolutionary–neural classification approach
inferred← citeshttps://doi.org/10.1007/11564089_5openalex-snapshot
Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources
inferred← citeshttps://doi.org/10.1007/11871842_60openalex-snapshot
Cost-Sensitive Decision Tree Learning for Forensic Classification
inferred← citeshttps://doi.org/10.1007/3-540-56004-1_4openalex-snapshot
Background knowledge and declarative bias in inductive concept learning
inferred← citeshttps://doi.org/10.1007/3-540-61532-6_3openalex-snapshot
Cost-sensitive specialization
inferred← citeshttps://doi.org/10.1007/978-1-4471-0835-1_10openalex-snapshot
On Rule Interestingness Measures
inferred← citeshttps://doi.org/10.1007/978-3-319-47557-8_11openalex-snapshot
Experiences Using Decision Trees for Knowledge Discovery
inferred← citeshttps://doi.org/10.1007/978-3-642-15387-7_54openalex-snapshot
Test-Cost Sensitive Classification on Data with Missing Values in the Limited Time
inferred← citeshttps://doi.org/10.1007/978-3-642-16493-4_46openalex-snapshot
Test-Cost Sensitive Classification Using Greedy Algorithm on Training Data
inferred← citeshttps://doi.org/10.1007/978-3-642-57678-2_48openalex-snapshot
Generalized regression trees1
inferred← citeshttps://doi.org/10.1007/978-981-99-7657-7_1openalex-snapshot
Introduction
inferred← citeshttps://doi.org/10.1007/bf00849079openalex-snapshot
Inductive learning: Algorithms and frontiers
inferred← citeshttps://doi.org/10.1007/bf00993101openalex-snapshot
Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics
inferred← citeshttps://doi.org/10.1007/bf00994659openalex-snapshot
Monotonicity Maintenance in Information-Theoretic Machine Learning Algorithms
inferred← citeshttps://doi.org/10.1007/bfb0052845openalex-snapshot
A systematic description of greedy optimisation algorithms for cost sensitive generalisation
inferred← citeshttps://doi.org/10.1007/s10844-010-0137-0openalex-snapshot
SEWEBAR-CMS: semantic analytical report authoring for data mining results
inferred← citeshttps://doi.org/10.1007/s10994-010-5228-1openalex-snapshot
Anytime learning of anycost classifiers
inferred← citeshttps://doi.org/10.1016/b978-1-4832-0774-2.50013-5openalex-snapshot
Toward Scaling Up Machine Learning: A Case Study with Derivational Analogy in PRODIGY
inferred← citeshttps://doi.org/10.1016/b978-1-55860-335-6.50022-2openalex-snapshot
Rule Induction for Semantic Query Optimization
inferred← citeshttps://doi.org/10.1016/b978-1-55860-377-6.50011-6openalex-snapshot
On Handling Tree-Structured Attributes in Decision Tree Learning
inferred← citeshttps://doi.org/10.1016/j.artint.2020.103400openalex-snapshot
Using POMDPs for learning cost sensitive decision trees
inferred← citeshttps://doi.org/10.1016/j.artmed.2015.07.005openalex-snapshot
A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification
inferred← citeshttps://doi.org/10.1016/j.asoc.2013.08.014openalex-snapshot
Cost-sensitive decision tree ensembles for effective imbalanced classification
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.108437openalex-snapshot
A reinforcement learning guided adaptive cost-sensitive feature acquisition method
inferred← citeshttps://doi.org/10.1016/j.comcom.2015.07.007openalex-snapshot
Predicting the attributes of social network users using a graph-based machine learning method
inferred← citeshttps://doi.org/10.1016/j.dam.2007.05.060openalex-snapshot
Multi-group support vector machines with measurement costs: A biobjective approach
inferred← citeshttps://doi.org/10.1016/j.datak.2006.06.018openalex-snapshot
Utilizing hierarchical feature domain values for prediction
inferred← citeshttps://doi.org/10.1016/j.dss.2004.07.001openalex-snapshot
Hybrid approaches for classification under information acquisition cost constraint
inferred← citeshttps://doi.org/10.1016/j.eswa.2011.02.144openalex-snapshot
Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2020.114246openalex-snapshot
A Neighborhood Undersampling Stacked Ensemble (NUS-SE) in imbalanced classification
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115902openalex-snapshot
Integration of aggressive bound tightening and Mixed Integer Programming for Cost-sensitive feature selection in medical diagnosis
inferred← citeshttps://doi.org/10.1016/j.ijproman.2008.04.003openalex-snapshot
A social ontology for appraising sustainability of construction projects and developments
inferred← citeshttps://doi.org/10.1016/j.ins.2008.11.032openalex-snapshot
Building a cost-constrained decision tree with multiple condition attributes
inferred← citeshttps://doi.org/10.1016/j.ins.2011.01.035openalex-snapshot
The CASH algorithm-cost-sensitive attribute selection using histograms
inferred← citeshttps://doi.org/10.1016/j.jss.2011.10.007openalex-snapshot
Decision tree classifiers sensitive to heterogeneous costs
inferred← citeshttps://doi.org/10.1016/j.knosys.2010.01.008openalex-snapshot
Cost-sensitive classification with respect to waiting cost
inferred← citeshttps://doi.org/10.1016/j.knosys.2015.10.008openalex-snapshot
Classification with test costs and background knowledge
inferred← citeshttps://doi.org/10.1016/j.knosys.2015.11.010openalex-snapshot
Cost-sensitive feature selection using random forest: Selecting low-cost subsets of informative features
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106145openalex-snapshot
A mixed integer linear programming support vector machine for cost-effective feature selection
inferred← citeshttps://doi.org/10.1016/j.patcog.2018.09.012openalex-snapshot
Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification
inferred← citeshttps://doi.org/10.1016/j.patcog.2023.109610openalex-snapshot
Learning Customised Decision Trees for Domain-knowledge Constraints
inferred← citeshttps://doi.org/10.1016/j.patrec.2010.05.028openalex-snapshot
Qualitative test-cost sensitive classification
inferred← citeshttps://doi.org/10.1016/j.patrec.2016.06.018openalex-snapshot
Beyond accuracy: Learning selective Bayesian classifiers with minimal test cost
inferred← citeshttps://doi.org/10.1016/j.procs.2014.08.111openalex-snapshot
Sequential Classification by Exploring Levels of Abstraction
inferred← citeshttps://doi.org/10.1016/s0169-023x(96)00003-1openalex-snapshot
A hierarchical model learning approach for refining and managing concept clusters discovered from databases
inferred← citeshttps://doi.org/10.1016/s0950-7051(99)00019-2openalex-snapshot
On rule interestingness measures
inferred← citeshttps://doi.org/10.1016/s0956-7135(99)00004-3openalex-snapshot
Computer-aided microbial identification using decision trees
inferred← citeshttps://doi.org/10.1093/comjnl/bxw015openalex-snapshot
A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework
inferred← citeshttps://doi.org/10.1108/jm2-03-2021-0065openalex-snapshot
Cost-sensitive meta-learning framework
inferred← citeshttps://doi.org/10.1109/69.634747openalex-snapshot
Sequential decision models for expert system optimization
inferred← citeshttps://doi.org/10.1109/access.2016.2552478openalex-snapshot
Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models
inferred← citeshttps://doi.org/10.1109/bhi.2012.6211629openalex-snapshot
Breast thermogram analysis using a cost-sensitive multiple classifier system
inferred← citeshttps://doi.org/10.1109/cadvis.1994.284520openalex-snapshot
Interactive learning of multiple attribute hash table for fast 3D object recognition
inferred← citeshttps://doi.org/10.1109/cbms.2007.59openalex-snapshot
Increasing Acceptability of Decision Trees with Domain Attributes Partial Orders
inferred← citeshttps://doi.org/10.1109/cec.2014.6900500openalex-snapshot
Cost-sensitive texture classification
inferred← citeshttps://doi.org/10.1109/cec.2015.7257177openalex-snapshot
Strategies for addressing class imbalance in ensemble classification of thermography breast cancer features
inferred← citeshttps://doi.org/10.1109/cimsa.2011.6059938openalex-snapshot
Effect of feature selection on machine learning algorithms for more accurate predictor of surgical outcomes in Benign Pro Static Hyperplasia cases (BPH)
inferred← citeshttps://doi.org/10.1109/cimsa.2012.6269609openalex-snapshot
Analysis of how the choice of Machine Learning algorithms affects the prediction of a clinical outcome prior to minimally invasive treatments for Benign Pro Static Hyperplasia BPH
inferred← citeshttps://doi.org/10.1109/cit.2008.workshops.51openalex-snapshot
A Strategy for Attributes Selection in Cost-Sensitive Decision Trees Induction
inferred← citeshttps://doi.org/10.1109/eit.2014.6871784openalex-snapshot
Decision tree classification algorithm based on cost and benefit dual-sensitive
inferred← citeshttps://doi.org/10.1109/gcis.2009.229openalex-snapshot
A Benefit-Cost Based Method for Cost-Sensitive Decision Trees
inferred← citeshttps://doi.org/10.1109/icacia.2008.4769964openalex-snapshot
Definition of Misclassification Cost & Redistribution Strategy of Telecom Churn Analysis
inferred← citeshttps://doi.org/10.1109/iccse.2019.8845414openalex-snapshot
A Cost-Sensitive Feature Selection Method for High-Dimensional Data
inferred← citeshttps://doi.org/10.1109/icdm.2004.10042openalex-snapshot
A Biobjective Model to Select Features with Good Classification Quality and Low Cost
inferred← citeshttps://doi.org/10.1109/icdm.2004.10092openalex-snapshot
Test-Cost Sensitive Naive Bayes Classification
inferred← citeshttps://doi.org/10.1109/icdm.2009.46openalex-snapshot
CoCoST: A Computational Cost Efficient Classifier
inferred← citeshttps://doi.org/10.1109/icdmw.2012.33openalex-snapshot
A Multi-armed Bandit Approach to Cost-Sensitive Decision Tree Learning
inferred← citeshttps://doi.org/10.1109/iciev.2017.8338552openalex-snapshot
Strategies for imbalanced pattern classification for digital pathology
inferred← citeshttps://doi.org/10.1109/icmlc.2015.7340902openalex-snapshot
Cost-sensitive decision tree with probabilistic pruning mechanism
inferred← citeshttps://doi.org/10.1109/iconip.1999.845635openalex-snapshot
An optimised decision tree induction algorithm for real world data domains
inferred← citeshttps://doi.org/10.1109/ijcnn.2005.1556167openalex-snapshot
Pruning support vectors for imbalanced data classification
inferred← citeshttps://doi.org/10.1109/intech.2017.8102455openalex-snapshot
Classifier ensembles for imbalanced classification
inferred← citeshttps://doi.org/10.1109/jcai.2009.118openalex-snapshot
Cost-sensitive Decision Tree with Missing Values and Multiple Cost Scales
inferred← citeshttps://doi.org/10.1109/jsac.2002.1003040openalex-snapshot
Automatic discovery of rules for predicting network management events
inferred← citeshttps://doi.org/10.1109/malware.2015.7413684openalex-snapshot
Automatically combining static malware detection techniques
inferred← citeshttps://doi.org/10.1109/saci.2013.6608995openalex-snapshot
Evolutionary optimisation of classifiers and classifier ensembles for cost-sensitive pattern recognition
inferred← citeshttps://doi.org/10.1109/saci.2013.6609012openalex-snapshot
A cost-sensitive ensemble classifier for breast cancer classification
inferred← citeshttps://doi.org/10.1109/tai.1993.633990openalex-snapshot
HML-An approach for refining/managing knowledge discovered from databases
inferred← citeshttps://doi.org/10.1109/tgrs.2012.2200044openalex-snapshot
A Partially Supervised Approach for Detection and Classification of Buried Radioactive Metal Targets Using Electromagnetic Induction Data
inferred← citeshttps://doi.org/10.1109/tkde.2002.1000348openalex-snapshot
An instance-weighting method to induce cost-sensitive trees
inferred← citeshttps://doi.org/10.1109/tkde.2005.176openalex-snapshot
Cost-constrained data acquisition for intelligent data preparation
inferred← citeshttps://doi.org/10.1109/tkde.2005.188openalex-snapshot
"Missing is useful": missing values in cost-sensitive decision trees
inferred← citeshttps://doi.org/10.1109/tkde.2006.131openalex-snapshot
Test strategies for cost-sensitive decision trees
inferred← citeshttps://doi.org/10.1109/tkde.2006.84openalex-snapshot
Test-cost sensitive classification on data with missing values
inferred← citeshttps://doi.org/10.1109/tkde.2019.2956530openalex-snapshot
Efficient Utilization of Missing Data in Cost-Sensitive Learning
inferred← citeshttps://doi.org/10.1109/tsmcc.2011.2157494openalex-snapshot
A Survey of Evolutionary Algorithms for Decision-Tree Induction
inferred← citeshttps://doi.org/10.1111/exsy.12495openalex-snapshot
EBNO: Evolution of cost‐sensitive Bayesian networks
inferred← citeshttps://doi.org/10.1111/j.1468-0394.2010.00515.xopenalex-snapshot
Cost‐sensitive methods of constructing hierarchical classifiers
inferred← citeshttps://doi.org/10.11606/t.55.2013.tde-21032014-144814openalex-snapshot
On the automatic design of decision-tree induction algorithms
inferred← citeshttps://doi.org/10.1177/0272989x10377117openalex-snapshot
Cost-Saving Tree-Structured Survival Analysis for Hip Fracture of Study of Osteoporotic Fractures Data
inferred← citeshttps://doi.org/10.1287/ijoc.11.3.278openalex-snapshot
Optimizing Expert Systems: Heuristics for Efficiently Generating Low-Cost Information Acquisition Strategies
inferred← citeshttps://doi.org/10.1287/isre.11.2.137.11777openalex-snapshot
Mean-Risk Trade-Offs in Inductive Expert Systems
inferred← citeshttps://doi.org/10.1609/aaai.v32i1.11779openalex-snapshot
MDP-Based Cost Sensitive Classification Using Decision Trees
inferred← citeshttps://doi.org/10.1609/aaai.v37i8.26180openalex-snapshot
Scalable Optimal Multiway-Split Decision Trees with Constraints
inferred← citeshttps://doi.org/10.1613/jair.120openalex-snapshot
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
inferred← citeshttps://doi.org/10.1613/jair.1512openalex-snapshot
Integrating Learning from Examples into the Search for Diagnostic Policies
inferred← citeshttps://doi.org/10.1613/jair.2602openalex-snapshot
Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach
inferred← citeshttps://doi.org/10.1613/jair.3200openalex-snapshot
Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition
inferred← citeshttps://doi.org/10.18130/v3p57wopenalex-snapshot
Simultaneous Feature Selection and Parameter Estimation for Hidden Markov Models
inferred← citeshttps://doi.org/10.21037/jtd.2019.12.119openalex-snapshot
A study of aortic dissection screening method based on multiple machine learning models
inferred← citeshttps://doi.org/10.2991/iccia.2012.437openalex-snapshot
A Strategy of Constructing Heterogeneous Cost-sensitive Decision Tree
inferred← citeshttps://doi.org/10.3156/jfuzzy.14.5_4openalex-snapshot
The past, the present, and the future of fuzzy inductive learning methods
inferred← citeshttps://doi.org/10.3182/20110828-6-it-1002.02522openalex-snapshot
Lower-bound Estimate for Cost-sensitive Decision Trees*
inferred← citeshttps://doi.org/10.3389/frai.2023.1124553openalex-snapshot
Decision trees: from efficient prediction to responsible AI
inferred← citeshttps://doi.org/10.3844/jcssp.2014.1151.1155openalex-snapshot
ITERATIVE DICHOTOMISER-3 ALGORITHM IN DATA MINING APPLIED TO DIABETES DATABASE
inferred← citeshttps://doi.org/10.4018/978-1-60566-218-3.ch003openalex-snapshot
Cost-Sensitive Learning in Medicine
inferred← citeshttps://doi.org/10.4018/978-1-60566-218-3.chcrfopenalex-snapshot
Compilation of References
inferred← citeshttps://doi.org/10.4018/978-1-60960-818-7.ch607openalex-snapshot
Cost-Sensitive Learning in Medicine
inferred← citeshttps://doi.org/10.4028/www.scientific.net/amr.756-759.3414openalex-snapshot
A Strategy of Constructing Heterogeneous Cost-Sensitive Decision Tree
inferred← citeshttps://doi.org/10.56583/br.1085openalex-snapshot
Extracting Decision Rules from Linguistic Data Describing Economic Phenomena. The Approach Based on Decision Systems over Ontological Graphs and PSO
inferred← citeshttps://doi.org/10.6028/nist.ir.7310openalex-snapshot
Evaluating reasoning systems
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