On mapping decision trees and neural networks

被引：27

作者：

Setiono, R ^{[1
]}

Leow, WK ^{[1
]}

机构：

[1] Natl Univ Singapore, Sch Comp, Singapore 119260, Singapore

来源：

KNOWLEDGE-BASED SYSTEMS | 1999年 / 12卷 / 03期

关键词：

decision trees; neural networks; pruning;

D O I：

10.1016/S0950-7051(99)00009-X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

There exist several methods for transforming decision trees to neural networks. These methods typically construct the networks by directly mapping decision nodes or rules to the neural units. As a result, the networks constructed are often larger than necessary. This article describes a pruning-based method for mapping decision trees to neural networks, which can compress the network by removing unimportant and redundant units and connections. In addition, equivalent decision trees extracted from the pruned networks are simpler than those induced by well-known algorithms such as ID3 and C4.5. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：95 / 99

页数：5

共 50 条

[41] Identification of radon anomalies in soil gas using decision trees and neural networks
Zmazek, Boris
Dzeroski, Saso
Torkar, Drago
Vaupotic, Janja
Kobal, Ivan
[J]. NUKLEONIKA, 2010, 55 (04) : 501 - 505
[42] Diversity between neural networks and decision trees for building multiple classifier systems
Wang, WJ
Jones, P
Partridge, D
[J]. MULTIPLE CLASSIFIER SYSTEMS, 2000, 1857 : 240 - 249
[43] Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search
Li, Pan
Qin, Zhen
Wang, Xuanhui
Metzler, Donald
[J]. KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, : 2032 - 2040
[44] Classification of haemoglobin typing chromatograms by neural networks and decision trees for thalassaemia screening
Piroonratana, Theera
Wongseree, Waranyu
Assawamakin, Anunchai
Paulkhaolarn, Nuttawut
Kanjanakorn, Chompunut
Sirikong, Monchan
Thongnoppakhun, Wanna
Limwongse, Chanin
Chaiyaratana, Nachol
[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2009, 99 (02) : 101 - 110
[45] Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees
Panchev, Christo
Dobrev, Petar
Nicholson, James
[J]. ENGINEERING APPLICATIONS OF NEURAL NETWORKS (EANN 2014), 2014, 459 : 175 - 182
[46] Derived operating rules for a reservoir operation system: Comparison of decision trees, neural decision trees and fuzzy decision trees
Wei, Chih-Chiang
Hsu, Nien-Sheng
[J]. WATER RESOURCES RESEARCH, 2008, 44 (02)
[47] Emergence of learning rule in neural networks using Genetic Programming combined with decision trees
Matsumoto, N
Tazaki, E
[J]. 1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5, 1998, : 1801 - 1805
[48] Genetic algorithms for optimisation of predictive ecosystems models based on decision trees and neural networks
D'heyere, Tom
Goethals, Peter L. M.
De Pauw, Niels
[J]. ECOLOGICAL MODELLING, 2006, 195 (1-2) : 20 - 29
[49] The use of artificial neural networks and decision trees: Implications for health-care research
Smith, Shaina
McConnell, Sabine
[J]. OPEN COMPUTER SCIENCE, 2024, 14 (01):
[50] Using Neural Networks and Ensemble Techniques based on Decision Trees for Skin Permeability Prediction
Busatlic, Emir
Osmanovic, Ahmed
Jakupovic, Alma
Nuhic, Jasna
Hodzic, Adnan
[J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING 2017 (CMBEBIH 2017), 2017, 62 : 41 - 50

← 1 2 3 4 5 →