Feature transformation methods in data mining

被引：69
作者：
Kusiak, A [1 ]
机构：
[1] Univ Iowa, Dept Mech & Ind Engn, Intelligent Syst Lab, Iowa City, IA 52242 USA
来源：
IEEE TRANSACTIONS ON ELECTRONICS PACKAGING MANUFACTURING | 2001年 / 24卷 / 03期
关键词：
classification; data mining; decision making; feature bundling; feature transformation method; knowledge discovery; transformed data set;
D O I：
10.1109/6104.956807
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The quality of knowledge extracted from a data set can be enhanced by its transformation. Discretization and filling missing data are the most common forms of data transformation. A new transformation method named feature bundling is introduced. A feature bundle involves a set of features in its pure or transformed form. The computational results reported in this paper show that the classification accuracy of decision rules generated from data sets with feature bundles is enhanced. The proposed concept of feature bundling is applied to a data set from semiconductor industry.
引用
收藏
页码：214 / 221
页数：8
相关论文
共 50 条
  • [21] Using data mining methods for manufacturing process control
    Vazan, P.
    Janikova, D.
    Tanuska, P.
    Kebisek, M.
    Cervenanska, Z.
    IFAC PAPERSONLINE, 2017, 50 (01): : 6178 - 6183
  • [22] Comparative analysis of data mining methods for bankruptcy prediction
    Olson, David L.
    Delen, Dursun
    Meng, Yanyan
    DECISION SUPPORT SYSTEMS, 2012, 52 (02) : 464 - 473
  • [23] The Estimation of Students' Academic Success by Data Mining Methods
    Goker, Hanife
    Bulbul, Halil Ibrahim
    Irmak, Erdal
    2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 2, 2013, : 535 - 539
  • [24] A Close Look at Privacy Preserving Data Mining Methods
    Wu, Xiaodan
    Wang, Yunfeng
    Chu, Chao-Hsien
    Liu, Fengli
    Chen, Ping
    Yue, Dianmin
    PACIFIC ASIA CONFERENCE ON INFORMATION SYSTEMS 2006, SECTIONS 1-8, 2006, : 167 - +
  • [25] Machine Learning and Data Mining Methods in Diabetes Research
    Kavakiotis, Ioannis
    Tsave, Olga
    Salifoglou, Athanasios
    Maglaveras, Nicos
    Vlahavas, Ioannis
    Chouvarda, Ioanna
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2017, 15 : 104 - 116
  • [26] A Taxonomy of Optimal Feature Learning Model in Combined Data Mining System
    Nivetha, S.
    Sheshasaayee, Ananthi
    2021 IEEE INTERNATIONAL CONFERENCE ON MOBILE NETWORKS AND WIRELESS COMMUNICATIONS (ICMNWC), 2021,
  • [27] Data Mining in Credit Card Approval: Feature Importance Testing Comparison
    Ye, Qingyu
    Fong, Simon
    Yu, Jiahui
    Tallon-Ballesteros, Antonio J.
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2024, PT II, 2025, 15347 : 543 - 554
  • [28] Application of Data Mining Algorithms for Feature Selection and Prediction of Diabetic Retinopathy
    Oladele, Tinuke O.
    Ogundokun, Roseline Oluwaseun
    Kayode, Aderonke Anthonia
    Adegun, Adekanmi Adeyinka
    Adebiyi, Marion Oluwabunmi
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT V: 19TH INTERNATIONAL CONFERENCE, SAINT PETERSBURG, RUSSIA, JULY 14, 2019, PROCEEDINGS, PART V, 2019, 11623 : 716 - 730
  • [29] Data types generalization and transformation for data mining algorithms
    Jiang, MF
    Tseng, SS
    Liao, SY
    ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS, 2000, 8 (02): : 89 - 95
  • [30] Comparison of data mining algorithms in remote sensing using Lidar data fusion and feature selection
    Rozario, Papia
    Gomes, Rahul
    2021 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT), 2021, : 236 - 243
12345