Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Algorithm Selection Using Performance and Run Time Behavior Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Weighted Feature Selection Method for Instance-Based Classification

Authors : Gennady Agre, Anton Dzhondzhorov

Published in: Artificial Intelligence: Methodology, Systems, and Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The paper presents a new method for selecting features that is suited for the instance-based classification. The selection is based on the ReliefF estimation of the quality of features in the orthogonal feature space obtained after PCA transformation, as well as on the interpretation of these weights as values proportional to the amount of explained concept changes. The user sets a threshold defining what percent of the whole concept variability the selected features should explain and only the first “stronger” features, which combine weights together exceed this threshold, are selected. During the classification phase the selected features are used along with their weights. The experiment results on 12 benchmark databases have shown the advantages of the proposed method in comparison with traditional ReliefF.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Algorithm Selection Using Performance and Run Time Behavior
next chapter Handling Uncertain Attribute Values in Decision Tree Classifier Using the Belief Function Theory
Footnotes
Literature
1.
Bins, J., Draper, B.: Feature selection from huge feature sets. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, vol. 2, pp. 159–165 (2001)
2.
Chang, C.-C.: Generalized iterative RELIEF for supervised distance metric learning. Pattern Recogn. 43(8), 2971–2981 (2010)CrossRefMATH
3.
Dhanabal, S., Chandramathi, S.: A review of various k-nearest neighbor query processing techniques. Intern. J. Comput. Appl. 31(7), 14–22 (2011)
4.
Diamataras, K.I., Kung, S.J.: Principal Component Neural Networks. Theory and Applications. Wiley, New York (1996)
5.
Florez-lopez, R.: Reviewing RELIEF and its extensions: a new approach for estimating attributes considering high-correlated features. In: Proceedings of IEEE International Conference on Data Mining, Maebashi, Japan, pp. 605–608 (2002)
6.
Freitag, D., Caruana, R.: Greedy attribute selection. In: Proceedings of Eleven International Conference on Machine Learning, pp. 28–36 (1994)
7.
Hall, M.A.: Correlation-based feature selection of discrete and numeric class machine learning. In: Proceedings of International Conference on Machine Learning (ICML-2000), San Francisco, CA, pp. 359–366. Morgan Kaufmann, San Francisco (2000)
8.
9.
Kira, K., Rendell, L.A.: The feature selection problem: traditional methods and a new algorithm. In: Proceedings of AAAI 1992, San Jose, USA, pp. 129–134 (1992)
10.
Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Proceedings of European Conference on Machine Learning, Catania, Italy, vol. 182, pp. 171–182 (1994)
11.
Ordonez, C., Mohanam, N., Garcia-Alvarado, C.: PCA for large data sets with parallel data summarization. Distrib. Parallel Databases 32(3), 377–403 (2014)CrossRef
12.
Pechenizkiy, M.: The impact of feature extraction on the performance of a classifier: kNN, Naïve Bayes and C4.5. In: Kégl, B., Lee, H.-H. (eds.) Canadian AI 2005. LNCS (LNAI), vol. 3501, pp. 268–279. Springer, Heidelberg (2005)CrossRef
13.
Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. J. 53, 23–69 (2003)CrossRefMATH
14.
Sharma, A., Paliwala, K., Onwubolu, G.: Class-dependent PCA, MDC and LDA: a combined classifier for pattern classification. Pattern Recogn. 39, 1215–1229 (2006)CrossRefMATH
15.
Strandjev, B., Agre, G.: On impact of PCA for solving classification tasks defined on facial images. Intern. J. Reason. Based Intell. Syst. 6(3/4), 85–92 (2014)
16.
Sun, Y., Li, J.: Iterative RELIEF for feature weighting: algorithms, theories, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1035–1051 (2007)MathSciNetCrossRef
17.
Tsymbal, A., Puuronen, S., Pechenizkiy, M., Baumgarten, M., Patterson, D.W.: Eigenvector-based feature extraction for classification. In: Proceedings of FLAIRS Conference, pp. 354–358 (2002)
18.
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRef
19.
Vergara, J., Estevez, P.: A review of feature selection methods based on mutual information. Neural Comput. Appl. 24, 175–186 (2014)CrossRef
20.
Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11, 273–314 (1997)CrossRef
21.
Yang, J., Li, Y.-P.: Orthogonal relief algorithm for feature selection. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4113, pp. 227–234. Springer, Heidelberg (2006)CrossRef
22.
Zeng, X., Wang, Q., Zhang, C., Cai, H.: Feature selection based on ReliefF and PCA for underwater sound classification. In: Proceedings of the 3rd International Conference on Computer Science and Network Technology (ICCSNT), Dalian, pp. 442–445 (2013)
Metadata
Title
A Weighted Feature Selection Method for Instance-Based Classification
Authors
Gennady Agre
Anton Dzhondzhorov
Copyright Year
2016
Book
Artificial Intelligence: Methodology, Systems, and Applications

Print ISBN: 978-3-319-44747-6

Electronic ISBN: 978-3-319-44748-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-44748-3_2

Premium Partner

    Image Credits