Skip to main content

IFS: An Incremental Feature Selection Method to Classify High-Dimensional Data

  • Conference paper
  • First Online:
Proceedings of International Conference on Data, Electronics and Computing (ICDEC 2022)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Included in the following conference series:

  • 82 Accesses


Feature selection (FS) is the problem of finding the most informative features that lead to optimal classification accuracy. In high-dimensional data classification, FS can save a significant amount of computation time as well as can help improve classification accuracy. An important issue in many applications is handling the situation where new instances arrive dynamically. A traditional approach typically handles this situation by recomputing the whole feature selection process on all instances, including new arrivals, an approach that is computationally very expensive and not feasible in many real-life applications. An incremental approach to feature selection is meant to address this issue. In this paper, we propose an effective feature selection method that incrementally scans the data once and computes credibility scores for the features with respect to the class labels. The effectiveness of the proposed method is evaluated on high-dimensional gene expression datasets using different machine learning classifiers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Sang B, Chen H, Yang L, Li T, Xu W (2021) Incremental feature selection using a conditional entropy based on fuzzy dominance neighborhood rough sets. IEEE Trans Fuzzy Syst

    Google Scholar 

  2. Brassard G, Bratley P (1996) Fundamentals of algorithmics. Prentice-Hall, Inc

    Google Scholar 

  3. Ren D, Fei C, Taoxin P, Neal S, Qiang S (2014) Feature selection inspired classifier ensemble reduction. IEEE Trans Cybern 44(8):1259–1268

    Article  Google Scholar 

  4. Hoque N, Bhattacharyya DK, Kalita JK (2014) Mifs-nd: a mutual information-based feature selection method. Exp Syst Appl 41(14):6371–6385

    Article  Google Scholar 

  5. Hughes G (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inform Theor 14(1):55–63

    Article  Google Scholar 

  6. Anil J, Douglas Z (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158

    Article  Google Scholar 

  7. Katakis I, Tsoumakas G, Vlahavas I (2006) Dynamic feature space and incremental feature selection for the classification of textual data streams. In: Knowledge discovery from data streams, pp 107–116

    Google Scholar 

  8. Yuh-Jye L, Chien-Chung C, Chia-Huang C (2008) Incremental forward feature selection with application to microarray gene expression data. J Biopharmaceut Stat 18(5):827–840

    Article  MathSciNet  Google Scholar 

  9. Huan L, Rudy S (1998) Incremental feature selection. Appl Intell 9(3):217–230

    Article  Google Scholar 

  10. Ruiz R, Riquelme JC, Aguilar-Ruiz JS (2006) Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recogn 39(12):2383–2392

    Article  Google Scholar 

  11. Shimon W, Peter S (2006) Evolutionary function approximation for reinforcement learning. J Mach Learn Res 7:877–917

    MathSciNet  MATH  Google Scholar 

  12. Robert W, Steven L, Yu L (2012) Embedded incremental feature selection for reinforcement learning. Technical report, DTIC Document

    Google Scholar 

Download references


The work is funded by UGC under Start-up-Grant (2021–2023) Order No. F.30-592/2021(BSR), Govt of India.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Nazrul Hoque .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hoque, N., Ahmed, H.A., Bhattacharyya, D.K. (2023). IFS: An Incremental Feature Selection Method to Classify High-Dimensional Data. In: Das, N., Binong, J., Krejcar, O., Bhattacharjee, D. (eds) Proceedings of International Conference on Data, Electronics and Computing. ICDEC 2022. Algorithms for Intelligent Systems. Springer, Singapore.

Download citation

Publish with us

Policies and ethics