An Improved Random Forest Algorithm for classification in an imbalanced dataset.

被引：0
|
作者：
Jose, Christy [1 ]
Gopakumar, G. [1 ]
机构：
[1] Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amritapuri Campus, Vallikavu, Kerala, India
来源：
2019 URSI ASIA-PACIFIC RADIO SCIENCE CONFERENCE (AP-RASC) | 2019年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Nowadays machine learning algorithms are being used extensively in industrial applications. Many a times these algorithms are modified and fine tuned so as to improve the current products and get better results. In this paper, we analyse an industrial problem that was put forward in the 'IDA 2016 challenge' and propose an improved solution over the best solution identified as part of the challenge.
引用
收藏
页数：4
相关论文
共 50 条
  • [21] An Improved KNN Algorithm Based on Minority Class Distribution for Imbalanced Dataset
    Zang, Bo
    Huang, Ruochen
    Wang, Lei
    Chen, Jianxin
    Tian, Feng
    Wei, Xin
    2016 INTERNATIONAL COMPUTER SYMPOSIUM (ICS), 2016, : 696 - 700
  • [22] An Improved Algorithm for SVMs Classification of Imbalanced Data Sets
    Castro, Cristiano Leite
    Carvalho, Mateus Araujo
    Braga, Antonio Padua
    ENGINEERING APPLICATIONS OF NEURAL NETWORKS, PROCEEDINGS, 2009, 43 : 108 - 118
  • [23] A novel Random Forest integrated model for imbalanced data classification problem
    Gu, Qinghua
    Tian, Jingni
    Li, Xuexian
    Jiang, Song
    KNOWLEDGE-BASED SYSTEMS, 2022, 250
  • [24] Classification Using Random Forest on Imbalanced Credit Card Transaction Data
    Aktar, Hafija
    Masud, Md Abdul
    Aunto, Nusrat Jahan
    Sakib, Syed Nazmus
    2021 3RD INTERNATIONAL CONFERENCE ON SUSTAINABLE TECHNOLOGIES FOR INDUSTRY 4.0 (STI), 2021,
  • [25] Imbalanced data classification based on DB-SLSMOTE and random forest
    Han, Qi
    Yang, Rui
    Wan, Zitong
    Chen, Shaozhi
    Huang, Mengjie
    Wen, Huiqing
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 6271 - 6276
  • [26] An improved deep forest classification algorithm
    Ding, Jiaman
    Liu, Cuihua
    Li, Runxin
    You, Jinguo
    Jia, Lianyin
    INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2022, 40 (04) : 305 - 314
  • [27] Imbalanced educational data classification: an effective approach with resampling and random forest
    Vo Thi Ngoc Chau
    Nguyen Hua Phung
    PROCEEDINGS OF 2013 IEEE RIVF INTERNATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION TECHNOLOGIES: RESEARCH, INNOVATION, AND VISION FOR THE FUTURE (RIVF), 2013, : 135 - 140
  • [28] MARGIN-BASED RANDOM FOREST FOR IMBALANCED LAND COVER CLASSIFICATION
    Feng, W.
    Boukir, S.
    Huang, W.
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 3085 - 3088
  • [29] Supervised Microalgae Classification in Imbalanced Dataset
    Correa, Iago
    Drews-, Paulo, Jr.
    de Souza, Marcio Silva
    Tavano, Virginia Maria
    PROCEEDINGS OF 2016 5TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2016), 2016, : 49 - 54
  • [30] Network Traffic Classification with Improved Random Forest
    Wang, Chao
    Xu, Tongge
    Qin, Xi
    2015 11TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), 2015, : 78 - 81
12345