nach oben

Journal of Intelligent Information Systems

Erschienen in:

01.12.2015

Optimizing text classification through efficient feature selection based on quality metric

verfasst von: Jean-Charles Lamirel, Pascal Cuxac, Aneesh Sreevallabh Chivukula, Kafil Hajlaoui

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2015

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Feature maximization is a cluster quality metric which favors clusters with maximum feature representation as regard to their associated data. In this paper we show that a simple adaptation of such metric can provide a highly efficient feature selection and feature contrasting model in the context of supervised classification. The method is experienced on different types of textual datasets. The paper illustrates that the proposed method provides a very significant performance increase, as compared to state of the art methods, in all the studied cases even when a single bag of words model is exploited for data description. Interestingly, the most significant performance gain is obtained in the case of the classification of highly unbalanced, highly multidimensional and noisy data, with a high degree of similarity between the classes.

Vorheriger Artikel Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds

Nächster Artikel Evaluation of an associative classifier based on position-constrained frequent/closed subtree mining

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

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 "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"

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

Since Feature recall is equivalent to the conditional probability P(g|p) and Feature precision is equivalent to the conditional probability P(p|g), this former strategy can be classified as an expectation maximization approach with respect to the original definition given by Dempster et al. (1977). Harmonic mean provides an additional influence to the lowest of the two values in the combination of feature recall and feature precision.

See Section 4 for more details on usual weighting schemes exploited on textual data.

The QUAERO project was initiated to meet multimedia content analysis requirements for consumers and professionals facing the rapid increase of accessible digital information. This collaborative research and development project focuses on the areas of automatic extraction of information, analysis, classification and usage of digital multimedia content for professionals and consumers. One specific subtask of the project is to develop automatic patents’ validation tools.

http://www.ncbi.nlm.nih.gov/pubmed/

http://web.ist.utl.pt/~acardoso/datasets/

http://www.research.att.com/~lewis/reuters21578.html

http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/

http://www.cs.waikato.ac.nz/ml/weka/

In terms of active variables (see Section 3 for details).

The computation is performed on Linux with a laptop equipped with Intel^®;Pentium^®; cpu B970 2.3Ghz and with 8Go standard memory.

http://www.quaero.org

http://www.oseo.fr/

Aha, D., & Kibler, D. (1991). Instance-based learning algorithms. Machine Learning, 6, 37–66.

Attik, M., Lamirel, J.-C., Al Shehabi, S. (2006). Clustering analysis for data with multiple labels. In Proceedings of the IASTED International conference on databases and applications (DBA). Innsbruck.

Bache, K., & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.MATHCrossRef

Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A. (2012). A review of feature selection methods on synthetic data. Knowledge and Information Systems, 1–37.

Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984). Classification and Regression Trees. Belmont: Wadsworth International Group.MATH

Chawla, N.V., Bowyer, K.V., Hall, L.O., Kegelmeyer, W.P. (2002). Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.MATH

Dash, M., & Liu, H. (2003). Consistency-based search in feature selection. Artificial Intelligence, 151(1), 155–176.MATHMathSciNetCrossRef

Daviet, H. (2009). Class-Add, une procédure de sélection de variables basée sur une troncature k-additive de l’ information mutuelle et sur une classification ascendante hiérarchique en pré-traitement. PhD, Université de Nantes, France.

Dempster, A. P., Laird, N. M., Rubin, D. B. (1977). Maximum likelihood for incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B, 39(1), 1–38.MATHMathSciNet

Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research, 3, 1289–1305.MATH

Good, P. (2006). Resampling methods, 3rd edn. Birkhauser.

Guyon, I., Weston, J., Barnhill, S., Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1), 389–422.MATHCrossRef

Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182.MATH

Hall, M.A., & Smith, L.A. (1999). Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In Proceedings of the 12th international florida artificial intelligence research society conference (pp. 235-239). AAAI Press.

Hajlaoui, K., Cuxac, P., Lamirel, J.C., Francois, C. (2012). Enhancing patent expertise through automatic matching with scientific papers. Discovery Science LNCS, 7569, 299–312.CrossRef

Ken Lang, K. (1995). Learning to filter netnews. In Proceedings of the 12th international conference on machine learning (pp. 331–339).

Kohavi, R., & John, G.H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273–324.MATHCrossRef

Kononenko, I. (1994). Estimating Attributes: Analysis and Extensions of RELIEF. European Conference on Machine Learning, 171–182.

Ladha, L., & Deepa, T. (2011). Feature selection methods and algorithms. International Journal on Computer Science and Engineering, 3(5), 1787–1797.

Lallich, S., & Rakotomalala, R. (2000). Fast Feature Selection Using Partial Correlation for Multi-valued Attributes. In D. A. Zighed, J. Komorowski, J. Zytkow (Eds.), Principles of data mining and knowledge discovery, 221-231. Lecture notes in computer science (pp. 1910). Berlin-Heidelberg: Springer.

Lamirel, J.-C., Al Shehabi, S., Francois, C., Hoffmann, M. (2004). New classification quality estimators for analysis of documentary information: application to patent analysis and web mapping. Scientometrics, 60(3).

Lamirel, J.-C., & Ta, A.P. (2008). Combination of hyperbolic visualization and graph-based approach for organizing data analysis results: an application to social network analysis. In Proceedings of the 4th international conference on webometrics, informetrics and scientometrics and 9th COLLNET meeting. Berlin.

Lamirel, J.-C., Ghribi, M., Cuxac, P. (2010). Unsupervised recall and precision measures: a step towards new efficient clustering quality indexes. In Proceedings of the 19th international conference on computational statistics (COMPSTAT’2010). Paris.

Lamirel, J.-C, Mall, R., Cuxac, P., Safi, G. (2011). Variations to incremental growing neural gas algorithm based on label maximization. In Proceedings of IJCNN 2011. San Jose.

Lamirel, J.-C. (2012). A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research. Scientometrics, 93, 151–166.CrossRef

Mejía-Lavalle, M., Sucar, E., Arroyo, G. (2006). Feature selection with a perceptron neural net. Feature selection for data mining: interfacing machine learning and statistics.

Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(11), 559–572.CrossRef

Platt, J. (1998). Fast training of support vector machines using sequential minimal optimization. In B. Schoelkopf, C. Burges, A. Smola (Eds.). Advances in kernel methods - support vector learning. MIT Press.

Porter, M.F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.CrossRef

Quinlan, R. (1993). C4.5: Programs for machine learning. San Mateo: Morgan Kaufmann.

Salton, G. (1971). Automatic processing of foreign language documents. Englewood Cliffs: Prentice-Hill.

Salton, G., & Buckley, C. (1988). Term weighting approaches in automatic text retrieval. Information Processing and Management, 24(5), 513–523.CrossRef

Schmid, H. (1994). Probabilistic part-of-speech tagging using decision trees. In Proceedings of international conference on new methods in language processing.

Witten, I.H., & Frank, E. (2005). Data mining: practical machine learning tools and techniques. Morgan Kaufmann.

Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: a fast correlation-based filter solution. ICML 2003, 856–863. Washington.

Zhang, T., & Oles, F.J. (2001). Text categorization based on regularized linear classification methods. Information Retrieval, 4(1), 5–31.MATHCrossRef

Titel: Optimizing text classification through efficient feature selection based on quality metric
verfasst von: Jean-Charles Lamirel
Pascal Cuxac
Aneesh Sreevallabh Chivukula
Kafil Hajlaoui
Publikationsdatum: 01.12.2015
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 3/2015
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-014-0317-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2015

Subspace clustering of data streams: new algorithms and effective evaluation measures

Objectively evaluating condensed representations and interestingness measures for frequent itemset mining

Evaluation of an associative classifier based on position-constrained frequent/closed subtree mining

Guest editor’s introduction: special issue on quality issues, measures of interestingness and evaluation of data mining models

Cost-based quality measures in subgroup discovery

Mining coverage patterns from transactional databases

Premium Partner