Enhancing SVM Performance for Time-Based Classification Prediction Through Feature Expansion: A Comparative Analysis with LSTM

被引:0
|
作者
Telkom University, Faculty of Informatics, Bandung, Indonesia [1 ]
机构
来源
关键词
Compilation and indexing terms; Copyright 2025 Elsevier Inc;
D O I
12th International Conference on Information and Communication Technology, ICoICT 2024
中图分类号
学科分类号
摘要
Optimal systems - Support vector machines
引用
收藏
相关论文
共 50 条
  • [1] Enhancing Classification Performance through FeatureBoostThyro: A Comparative Study of Machine Learning Algorithms and Feature Selection
    Bhende, Deepali
    Sakarkar, Gopal
    Khandar, Punam
    Uparkar, Satyajit
    Bhave, Arvind
    INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING, 2024, 20 (04) : 29 - 42
  • [2] Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM
    Liu, Feixiang
    Yang, Mei
    Ke, Jie
    ADVANCES IN MECHANICAL ENGINEERING, 2024, 16 (05)
  • [3] Time-based competence and performance: an empirical analysis
    Al Serhan, Yahya N.
    Julian, Craig C.
    Ahmed, Zafar
    JOURNAL OF SMALL BUSINESS AND ENTERPRISE DEVELOPMENT, 2015, 22 (02) : 288 - +
  • [4] TEXT CLASSIFICATION THROUGH TIME Efficient Label Propagation in Time-Based Graphs
    Baluja, Shumeet
    Ravichandran, Deepak
    Sivakumar, D.
    KDIR 2009: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND INFORMATION RETRIEVAL, 2009, : 174 - 182
  • [5] A Comparative Analysis of SVM and ELM Classification on Software Reliability Prediction Model
    Rath, Suneel Kumar
    Sahu, Madhusmita
    Das, Shom Prasad
    Bisoy, Sukant Kishoro
    Sain, Mangal
    ELECTRONICS, 2022, 11 (17)
  • [6] Prediction of Bearing Performance Degradation with Bottleneck Feature based on LSTM Network
    Tang, Gang
    Zhou, Youguang
    Wang, Huaqing
    Li, Guozheng
    2018 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC): DISCOVERING NEW HORIZONS IN INSTRUMENTATION AND MEASUREMENT, 2018, : 804 - 809
  • [7] A comparative performance analysis of different activation functions in LSTM networks for classification
    Amir Farzad
    Hoda Mashayekhi
    Hamid Hassanpour
    Neural Computing and Applications, 2019, 31 : 2507 - 2521
  • [8] A comparative performance analysis of different activation functions in LSTM networks for classification
    Farzad, Amir
    Mashayekhi, Hoda
    Hassanpour, Hamid
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (07): : 2507 - 2521
  • [9] Predicting student performance using sequence classification with time-based windows
    Deeva, Galina
    De Smedt, Johannes
    Saint-Pierre, Cecilia
    Weber, Richard
    De Weerdt, Jochen
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 209
  • [10] Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China
    Li, Qiang
    Yang, Yinqun
    Yang, Ling
    Wang, Yonggui
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (03) : 7498 - 7509