Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2018

24.01.2018

An Improved Firefly Algorithm for Feature Selection in Classification

verfasst von: Huali Xu, Shuhao Yu, Jiajun Chen, Xukun Zuo

Erschienen in: Wireless Personal Communications | Ausgabe 4/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Feature selection functions as an important method of receiving data so as to make the amount of features decrease. While solving the issue of classifying there exists numerous features having no relevance and being unnecessary which have the potential of making classification performance decrease. Firefly algorithm (FA) functions as an efficient method to make computation which is efficient and progressive. Nevertheless, the conventional FA is easily fallen into the local optima which imposes unsatisfactory practice on feature selection. In this research, one proposal was put forward, the firefly algorithm that combines the binary firefly algorithm with opposition-based learning to select features in classification. Experiment outcomes indicate the fact that the means put forward surpasses PSO and the conventional firefly algorithm.
Vorheriger Artikel WB-RAIS: White-Box Redundant Array of Independent SSDs
Nächster Artikel A FOA-Optimized RBF Algorithm-Based Evaluation Research on E-commerce Websites

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Xu, Z., Jin, R., Lyu, M. R., & King, I. (2009). Non-monotonic feature selection. In Proceedings of the 26th international conference on machine learning, ICML (pp. 1145–1152).
2.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.MATH
3.
Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1(3), 131–156.CrossRef
4.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.
5.
Liu, L., Kang, J., Yu, J., & Wang, Z. (2005). A comparative study on unsupervised feature selection methods for text clustering. In Proceedings of 2005 IEEE international conference on natural language processing and knowledge engineering, IEEE NLP-KE’05 (pp. 597–601).
6.
Yang, Y. (1995). Noise reduction in a statistical approach to text categorization. In Proceedings of the 18th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR Forum (pp. 256–263).
7.
Li, Y., Luo, C., & Chung, S. M. (2008). Text clustering with feature selection by using statistical data. IEEE Transactions on Knowledge and Data Engineering, 20(5), 641–652.CrossRef
8.
Mengle, S. S., & Goharian, N. (2009). Measure feature-selection algorithm. Journal of the American Society for Information Science and Technology, 60(5), 1037–1050.CrossRef
9.
Pudil, P., Novoviˇcová, J., & Kittler, J. (1994). Floating search methods in feature selection. Pattern Recognition Letters, 15(11), 1119–1125.CrossRef
10.
Nakariyakul, S., & Casasent, D. P. (2009). An improvement on floating search algorithms for feature subset selection. Pattern Recognition, 42(9), 1932–1940.CrossRefMATH
11.
Somol, P., Pudil, P., Novoviˇcová, J., & Paclık, P. (1999). Adaptive floating search methods in feature selection. Pattern Recognition Letters, 20(11), 1157–1163.CrossRef
12.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.MATH
13.
Langley, P. (1994). Selection of relevant features in machine learning. In Proceedings of the AAAI Fall symposium on relevance.
14.
Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1–2), 245–271.MathSciNetCrossRefMATH
15.
Lin, S. W., Ying, K. C., Chen, S. C. H., & Lee, Z. J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 35(4), 1817–1824.CrossRef
16.
Neshatian, Kh. & Zhang, M. J. (2009). Genetic programming for feature subset ranking in binary classification problems. In Proceedings of the 12th European conference on genetic programming (EuroGP 2009), Lecture notes in computer science, 5481, 121–132.
17.
Colorni, A., Dorigo, M., Maniezzo, V., et al. (1991). Distributed optimization by ant colonies. Proceedings of the First European Conference on Artificial Life, 142, 134–142.
18.
Xue, B., Zhang, M. J., & Browne, W. N. (2013). Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics, 43(6), 1656–1671.CrossRef
19.
Yang, X. S. (2010). Firefly algorithm stochastic test functions and design optimization. International Journal of Bio-Inspired Computation, 2(2), 78–84.CrossRef
20.
Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In 5th symposium on stochastic algorithms, foundations and applications, SAGA 2009 (pp 169–178).
21.
Łukasik, S. & Zak, S. (2009). Firefly algorithm for continuous constrained optimization tasks. In Computational collective intelligence: Semantic web, social networks and multiagent systems, ICCCI 2009 (pp 97–106) Springer.
22.
Yang, X. S., Hosseini, S. S. S., & Gandomi, A. H. (2012). Firefly algorithm for solving non-convex economic dispatch problems with valveloading effect. Applied Soft Computing, 12(3), 1180–1186.CrossRef
23.
Senthilnath, J., Omkar, S. N., & Mani, V. (2011). Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation, 1(3), 164–171.CrossRef
24.
Fister, I. Jr., Yang, X. S., & Brest J. (2012). Memetic firefly algorithm for combinatorial optimization. In 5th international conference on bioinspired optimization methods and their applications, BIOMA 2012, (pp 75–86).
25.
Horng, M. H. (2012). Vector quantization using the firefly algorithm for image compression. Expert Systems with Applications, 39(1), 1078–1091.CrossRef
Metadaten
Titel
An Improved Firefly Algorithm for Feature Selection in Classification
verfasst von
Huali Xu
Shuhao Yu
Jiajun Chen
Xukun Zuo
Publikationsdatum
24.01.2018
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5309-1

Weitere Artikel der Ausgabe 4/2018

Wireless Personal Communications 4/2018 Zur Ausgabe

OriginalPaper

Research on Attribute Dimension Partition Based on SVM Classifying and MapReduce

OriginalPaper

Research on Monitoring Platform of Agricultural Product Circulation Efficiency Supported by Cloud Computing

OriginalPaper

Application of ArcGIS in Geography Teaching of Secondary School: A Case Study in the Practice of Map Teaching

OriginalPaper

Financial Management and Decision Based on Decision Tree Algorithm

OriginalPaper

Simulation Design of Collimation Zoom Optical System Based on Laser Ranging

OriginalPaper

Application of Data Mining Technology in Financial Risk Analysis

Neuer Inhalt

    Bildnachweise