about
Risk segmentation in Chilean social health insurance.In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning.Morphological Neuron Classification Using Machine LearningDiversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype.Fuzzy association rule mining and classification for the prediction of malaria in South Korea.Mild loss of lung aeration augments stretch in healthy lung regions.The use of the decision tree technique and image cytometry to characterize aggressiveness in World Health Organization (WHO) grade II superficial transitional cell carcinomas of the bladder.Applying machine learning techniques to the identification of late-onset hypogonadism in elderly men.Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.Contralateral artery enlargement predicts carotid plaque progression based on machine learning algorithm models in apoE-/- mice.Logic programming reveals alteration of key transcription factors in multiple myeloma.A novel feature ranking method for prediction of cancer stages using proteomics data.Using the ID3 algorithm to find discrepant diagnoses from laboratory databases of thyroid patients.On optimal settings of classification tree ensembles for medical decision support.Automatic design of decision-tree algorithms with evolutionary algorithms.Bounding the effect of noise in multiobjective learning classifier systems.Self-adaptive MOEA feature selection for classification of bankruptcy prediction data.Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.An experimental evaluation of comprehensibility aspects of knowledge structures derived through induction techniques: A case study of industrial fault diagnosisBoosted Decision Trees and ApplicationsMultivariate process monitoring and fault identification using multiple decision tree classifiersOnline monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniquesAutomatic extraction of metadata from scientific publications for CRIS systemsArguing from Experience to Classifying Noisy DataAN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREESEVOLVING NEURAL-SYMBOLIC SYSTEMS GUIDED BY ADAPTIVE TRAINING SCHEMES: APPLICATIONS IN FINANCEFuzzy Intelligent System for Patients with Preeclampsia in Wearable DevicesMissing-Values Adjustment for Mixed-Type DataAn Optimal Classification Method for Biological and Medical DataAnalysis of Changes in Market Shares of Commercial Banks Operating in Turkey Using Computational Intelligence AlgorithmsDecision Tree Classification Model for Popularity Forecast of Chinese CollegesWeight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification
P2860
Q30354994-A5E1464B-9FDA-4BF5-998E-3DF8AB7E4BB7Q30393617-7A9E3B36-CBBB-4F08-9026-870995311022Q30826985-32E5C900-CD96-4AF8-A5FD-BF2CD58D1297Q34032267-B3B1C98F-ACF8-4F9E-8C91-AE6ED9FFA3E2Q35666585-FADE7406-2682-4A21-9183-39028374A57BQ36582996-7318DF40-9150-439A-9ECF-EC4681FFF1F3Q36816052-51184CE5-331A-45D1-8C18-F7C8271C34CCQ37009272-0F682BCA-C282-454C-ADD5-B082848BA574Q37587950-E43C4144-A949-48EB-8487-4BCEF3C1E005Q37602324-A7EDC752-B595-4479-9585-783CCCB2FA24Q38611142-27FC2AB1-57C9-40A0-A589-5AEFD15859FBQ42362058-E64C4996-615B-421F-9A8D-79B67DD07EEFQ43815204-FC6220E4-CB10-4E4F-AE91-403132561EE6Q43866633-DCDC43D3-46FA-4FEE-80B9-9E04EB7C33E4Q46180243-844C2139-9BDC-443C-BE7E-5DFCF8A69BEEQ47443187-EB4C60F6-F347-4C24-8C2C-C2CE46ADBEFBQ51096156-F9507A4E-15A2-4FAC-803A-FA95708CD1A3Q53421291-E4890AC2-0F81-40CA-8282-F2868691BC1BQ56893645-EACA14EE-017E-499D-994A-33F03731077FQ57589443-3E3C2F5C-70DC-4FDE-8B8E-C8C23C785B74Q57734783-2DDF04FF-F742-4CC7-9960-44EBA67EE6B1Q57734786-F183DC83-1DE1-499B-97F1-19DF91E6882DQ57953061-AB7B0F6D-BEF7-4748-B600-5AA100092A00Q58168471-76824768-32A7-48EC-AFCA-C7A9D08BF262Q58252025-840CBD8B-BF49-43DA-9D79-4376B9AA4E83Q58269051-202105DB-A00A-4E19-A16D-8DE0C1206064Q58667285-444DC311-B7D8-47CD-9389-2009F2029B83Q58692006-A8321B61-AC3C-4243-BF14-0E0E19EF2F95Q58911532-16AB4688-5B7F-468D-9268-B30A93491414Q59042389-CB73D51B-6DE7-49EA-A92D-23BA40830149Q59054004-90ED8F34-5204-48F1-80D2-3222AAE0758AQ59119718-5906A500-F730-4511-B8B0-21389D23E056
P2860
description
wetenschappelijk artikel
@nl
наукова стаття, опублікована у вересні 1987
@uk
name
Simplifying decision trees
@en
Simplifying decision trees
@nl
type
label
Simplifying decision trees
@en
Simplifying decision trees
@nl
prefLabel
Simplifying decision trees
@en
Simplifying decision trees
@nl
P1476
Simplifying decision trees
@en
P2093
J.R. Quinlan
P304
P356
10.1016/S0020-7373(87)80053-6
P577
1987-09-01T00:00:00Z