Evaluation of the Classifiers in Multiparameter and Imbalanced Data Sets

被引：0
作者：
Piotrowska, Ewelina [1 ]
机构：
[1] Opole Univ Technol, Proszkowska 76 St, PL-45758 Opole, Poland
来源：
INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, ISAT 2019, PT II | 2020年 / 1051卷
关键词：
Imbalanced data; Optimization methods; Classification;
D O I：
10.1007/978-3-030-30604-5_24
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper discusses the basic problems resulting from the classification of imbalanced data, which are additionally described by a large number of parameters. The paper also presents various optimization methods, including the use of a synthetic indicator that is the product of specificity and the power of sensitivity, which was proposed by the author.
引用
收藏
页码：263 / 273
页数：11
相关论文
共 50 条
  • [31] Clustering Based Bagging Algorithm on Imbalanced Data Sets
    Sun, Xiao-Yan
    Zhang, Hua-Xiang
    Wang, Zhi-Chao
    INTEGRATED UNCERTAINTY IN KNOWLEDGE MODELLING AND DECISION MAKING, 2011, 7027 : 179 - 186
  • [32] Linear Approximation of F-Measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets
    Wong, Tzu-Tsung
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 34 (02) : 753 - 763
  • [33] Learning from Imbalanced Data Sets with Weighted Cross-Entropy Function
    Aurelio, Yuri Sousa
    de Almeida, Gustavo Matheus
    de Castro, Cristiano Leite
    Braga, Antonio Padua
    NEURAL PROCESSING LETTERS, 2019, 50 (02) : 1937 - 1949
  • [34] Preliminary Evaluation of Classification Complexity Measures on Imbalanced Data
    Xing, Yan
    Cai, Hao
    Cai, Yanguang
    Hejlesen, Ole
    Toft, Egon
    PROCEEDINGS OF 2013 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT INFORMATION PROCESSING, 2013, 256 : 189 - 196
  • [35] Evaluation of single-cell classifiers for single-cell RNA sequencing data sets
    Zhao, Xinlei
    Wu, Shuang
    Fang, Nan
    Sun, Xiao
    Fan, Jue
    BRIEFINGS IN BIOINFORMATICS, 2020, 21 (05) : 1581 - 1595
  • [36] Extension of ICF classifiers to real world data sets
    Haraguchi, Kazuya
    Nagamochi, Hiroshi
    NEW TRENDS IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2007, 4570 : 776 - +
  • [37] Application of Parallel Distributed Genetics-based Machine Learning to Imbalanced Data Sets
    Nojima, Yusuke
    Mihara, Shingo
    Ishibuchi, Hisao
    2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2012,
  • [38] Types of minority class examples and their influence on learning classifiers from imbalanced data
    Napierala, Krystyna
    Stefanowski, Jerzy
    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2016, 46 (03) : 563 - 597
  • [39] Editing Training Sets from Imbalanced Data Using Fuzzy-Rough Sets
    Nguyen, Do Van
    Ogawa, Keisuke
    Matsumoto, Kazunori
    Hashimoto, Masayuki
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, 2015, 458 : 115 - 129
  • [40] Feature selection for imbalanced data based on neighborhood rough sets
    Chen, Hongmei
    Li, Tianrui
    Fan, Xin
    Luo, Chuan
    INFORMATION SCIENCES, 2019, 483 : 1 - 20
12345