| |
| |
|
|
|
|
| Unsupervised Feature Selection Based on the Distribution of Features Attributed to Imbalanced Data Sets
|
|
Full
text: |
 PDF(133.3KB) |
|
|
Source |
International Journal of Artificial Intelligence and Expert Systems (IJAE) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(1.18MB) |
|
Volume: 2 Issue: 1 |
| |
Pages: 1-22 |
|
Publication
Date: March / April 2011 |
|
ISSN
(Online): 2180-124X |
|
|
|
|
|
Pages |
14 - 22 |
|
Author(s) |
|
|
|
Published
Date |
04-04-2011 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Feature, Feature Selection, Filter Approach, Imbalanced Data Set |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Scribd |
| 2. Docstoc |
| 3. Google Scholar |
| |
|
| |
|
|
| Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications.
In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
|
| |
|
| |
|
| |
| 1 |
U. Fayyad, G. Piatetsky-Shapiro and P. Smyth. “From data mining to knowledge discovery in databases”, AI Magazine, vol. 17, pp. 37–54, 1996
|
|
|
| 2 |
M. Lindenbaum, S. Markovitch and D. Rusakov. “Selective sampling for nearest neighbor classifiers”, Machine learning, vol. 54, pp. 125–152, 2004
|
|
|
| 3 |
A.I. Schein and L.H. Ungar, “Active learning for logistic regression: an evaluation”, Machine Learning, vol. 68, pp. 235–265, 2007
|
|
|
| 4 |
M.A. Hall. “Correlation-based feature subset selection for machine learning”, Ph.D. Dissertation, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 1999
|
|
|
| 5 |
I.K. Fodor. “A survey of dimension reduction techniques”, Technical Report UCRL- ID- 148494, Lawrence Livermore National Laboratory, US Department of Energy, 2002
|
|
|
| 6 |
M.A. Hall. “Correlation-based feature selection for discrete and numeric class machine learning”, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 2000
|
|
|
| 7 |
R. Bellman. “Adaptive Control Processes: A Guided Tour”, Princeton University Press, Princeton, 1961
|
|
|
| 8 |
H. Liu, J. Sun, L. Liu and H. Zhang, “Feature selection with dynamic mutual information”, Pattern Recognition, vol. 42, pp. 1330 – 1339, 2009
|
|
|
| 9 |
N. Pradhananga. “Effective Linear-Time Feature Selection”, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 2007
|
|
|
| 10 |
M. Wasikowski and X. Chen. “Combating the small sample class imbalance problem using feature selection”, IEEE Transactions on knowledge and data engineering, 2009
|
|
|
| 11 |
G.H. John and P. Langley. “Estimating Continuous Distributions in Bayesian Classifiers”. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338-345, 1995
|
|
|
| 12 |
M.P. Narendra and K. Fukunaga. “A branch and bound algorithm for feature subset selection”, IEEE Trans. Comput. Vol. 26, pp. 917–922, 1997
|
|
|
| 13 |
P.A. Devijver and J. Kittler. “Pattern Recognition: A Statistical Approach”, Englewood Cliffs: Prentice Hall, 1982
|
|
|
| 14 |
M. Dash and H. Liu. “Unsupervised Feature Selection”, Proc. Pacific Asia conf. Knowledge Discovery and Data Mining, pp. 110-121, 2000
|
|
|
| 15 |
J. Dy and C. Btodley. “Feature Subset Selection and Order Identification for Unsupervised Learning”, Proc. 17th Int’l. Conf. Machine Learning, 2000
|
|
|
| 16 |
S.Basu, C.A. Micchelli and P. Olsen. “Maximum Entropy and Maximum Likelihood Criteria for Feature Selection from Multi-variate Data”, Proc. IEEE Int’l. Symp. Circuits and Systems, pp. 267-270, 2000
|
|
|
| 17 |
S.K .Pal, R.K. De and J. Basak. “Unsupervised Feature Evaluation: A Neuro-Fuzzy Approach”, IEEE Trans. Neural Network, vol. 11, pp. 366-376, 2000
|
|
|
| 18 |
S.K .Das. “Feature Selection with a Linear Dependence Measure”, IEEE Trans. Computers, pp. 1106-1109, 1971
|
|
|
| 19 |
G.T. Toussaint and T.R. Vilmansen. “Comments on Feature Selection with a Linear Dependence Measure”, IEEE Trans. Computers, 408, 1972
|
|
|
| 20 |
H. Liu and R. Setiono. “A probabilistic approach to feature selection - A filter solution”. In: 13th International Conference on Machine Learning, pp. 319-327, 1996
|
|
|
| 21 |
K. Fukunaga. “Introduction to Statistical Pattern Recognition”, Academic Press, 2nd Ed. 1990
|
|
|
| 22 |
E. Frank, M.A. Hall, G. Holmes, R. Kirkby and B. Pfahringer. “Weka - a machine learning workbench for data mining”, In The Data Mining and Knowledge Discovery Handbook, pp. 1305-1314, 2005
|
|
|
| 23 |
M. Dash and H. Liu. “Unsupervised Feature Selection”, Proc. Pacific Asia Conf. Knowledge Discovery and Data Mining, pp. 110-121, 2000
|
|
|
| 24 |
P. Pudil, J. Novovicova and J. Kittler. “Floating Search Methods in Feature Selection”, Pattern Recognition Letters, vol. 15, pp. 1119-1125, 1994
|
|
|
| 25 |
R.O. Duda, P.E. Hart and D.G. Stork. “Pattern Classification”, Second Edition, Wiley, 1997
|
|
|
| 26 |
G. Forman. "An extensive empirical study of feature selection metrics for text classification", Journal of Machine Learning Research, vol. 3, pp. 1289-1305, 2003
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| Mina Alibeigi : Colleagues
|
|
| Sattar Hashemi : Colleagues
|
|
| Ali Hamzeh : Colleagues
|
|
|
|
|
|
|
|
|
|
|
