Generalized conditional entropy and a metric splitting criterion for decision trees

被引：0

作者：

Simovici, Dan A. ^{[1
]}

Jaroszewicz, Szymon

机构：

[1] Univ Massachusetts, Dept Comp Sci, Boston, MA 02125 USA

[2] Tech Univ Szczecin, Fac Comp & Informat Syst, Szeczin, Poland

来源：

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2006年 / 3918卷

关键词：

decision tree; generalized conditional entropy; metric; metric betweenness;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We examine a new approach to building decision tree by introducing a geometric splitting criterion, based on the properties of a family of metrics on the space of partitions of a finite set. This criterion can be adapted to the characteristics of the data sets and the needs of the users and yields decision trees that have smaller sizes and fewer leaves than the trees built with standard methods and have comparable or better accuracy.

引用

页码：35 / 44

页数：10

共 50 条

[1] A new metric splitting criterion for decision trees
Simovici, Dan A.
Jaroszewicz, Szymon
INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS, 2006, 21 (04) : 239 - 256
[2] Decision Trees for Continuous Data and Conditional Mutual Information as a Criterion for Splitting Instances
Drakakis, Georgios
Moledina, Saadiq
Chomenidis, Charalampos
Doganis, Philip
Sarimveis, Haralambos
COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2016, 19 (05) : 423 - 428
[3] A Discriminative Splitting Criterion for Phonetic Decision Trees
Wiesler, Simon
Heigold, Georg
Nussbaum-Thom, Markus
Schlueter, Ralf
Ney, Hermann
11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2, 2010, : 54 - 57
[4] A discriminative splitting criterion for phonetic decision trees
Wiesler, Simon
Heigold, Georg
Nußbaum-Thom, Markus
Schlüter, Ralf
Ney, Hermann
Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010, 2010, : 54 - 57
[5] Hybrid Splitting Criterion in Decision Trees for Data Stream Mining
Jaworski, Maciej
Rutkowski, Leszek
Pawlak, Miroslaw
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, (ICAISC 2016), PT II, 2016, 9693 : 60 - 72
[6] A parallel tree node splitting criterion for fuzzy decision trees
Mu, Yashuang
Liu, Xiaodong
Wang, Lidong
Asghar, Aamer Bilal
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2019, 31 (17):
[7] Testing Modified Confusion Entropy as Split Criterion for Decision Trees
David Nunez-Gonzalez, J.
Gonzalo de Sa, Alexander
Grana, Manuel
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2019, 2019, 11734 : 3 - 13
[8] Improving Decision Trees by Tsallis Entropy Information Metric Method
Wang, Yisen
Song, Chaobing
Xia, Shu-Tao
2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 4729 - 4734
[9] UNIFYING ATTRIBUTE SPLITTING CRITERIA OF DECISION TREES BY TSALLIS ENTROPY
Wang, Yisen
Xia, Shu-Tao
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 2507 - 2511
[10] A Novel Splitting Criterion Inspired by Geometric Mean Metric Learning for Decision Tree
Li, Dan
Chen, Songcan
2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2022, : 4808 - 4814

← 1 2 3 4 5 →