Decision Rule Classifiers for Multi-label Decision Tables

被引：0
|
作者：
Alsolami, Fawaz [1 ]
Azad, Mohammad [1 ]
Chikalov, Igor [1 ]
Moshkov, Mikhail [1 ]
机构：
[1] King Abdullah Univ Sci & Technol, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia
来源：
ROUGH SETS AND INTELLIGENT SYSTEMS PARADIGMS, RSEISP 2014 | 2014年 / 8537卷
关键词：
decision rules; rule heuristics; classification;
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：
Recently, multi-label classification problem has received significant attention in the research community. This paper is devoted to study the effect of the considered rule heuristic parameters on the generalization error. The results of experiments for decision tables from UCI Machine Learning Repository and KEEL Repository show that rule heuristics taking into account both coverage and uncertainty perform better than the strategies taking into account a single criterion.
引用
收藏
页码：191 / 197
页数：7
相关论文
共 50 条
  • [41] Predicting gene function using hierarchical multi-label decision tree ensembles
    Schietgat, Leander
    Vens, Celine
    Struyf, Jan
    Blockeel, Hendrik
    Kocev, Dragi
    Dzeroski, Saso
    BMC BIOINFORMATICS, 2010, 11
  • [42] Information quantity-based decision rule acquisition from decision tables
    Sun, Lin
    Xu, Jiucheng
    Song, Yanpei
    Journal of Convergence Information Technology, 2012, 7 (02) : 57 - 67
  • [43] Optimization of Decision Rule Complexity for Decision Tables with Many-Valued Decisions
    Azad, Mohammad
    Chikalov, Igor
    Moshkov, Mikhail
    2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013), 2013, : 444 - 448
  • [44] Multilabel classification using heterogeneous ensemble of multi-label classifiers
    Tahir, Muhammad Atif
    Kittler, Josef
    Bouridane, Ahmed
    PATTERN RECOGNITION LETTERS, 2012, 33 (05) : 513 - 523
  • [45] A Novel Approach for Multi-label Classification using Probabilistic Classifiers
    Kommu, Gangadhara Rao
    Trupthi, M.
    Pabboju, Suresh
    2014 INTERNATIONAL CONFERENCE ON ADVANCES IN ENGINEERING AND TECHNOLOGY RESEARCH (ICAETR), 2014,
  • [46] Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
    Melacci, Stefano
    Ciravegna, Gabriele
    Sotgiu, Angelo
    Demontis, Ambra
    Biggio, Battista
    Gori, Marco
    Roli, Fabio
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022, 44 (12) : 9944 - 9959
  • [47] Improved multi-label classifiers for predicting protein subcellular localization
    Chen, Lei
    Qu, Ruyun
    Liu, Xintong
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2024, 21 (01) : 214 - 236
  • [48] An Efficient Multi-Label Classification System Using Ensemble of Classifiers
    Chandran, Shilpa A.
    Panicker, Janu R.
    2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, INSTRUMENTATION AND CONTROL TECHNOLOGIES (ICICICT), 2017, : 1133 - 1136
  • [49] Aggregating Independent and Dependent Models to Learn Multi-label Classifiers
    Montanes, Elena
    Ramon Quevedo, Jose
    Jose del Coz, Juan
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT II, 2011, 6912 : 484 - 500
  • [50] CONVERSION OF DECISION TABLES BY RULE MASK METHOD WITHOUT RULE MASK
    DATHE, G
    COMMUNICATIONS OF THE ACM, 1972, 15 (10) : 906 - &
12345