DocumentCode :
1948858
Title :
Random Feature Subset Selection for Analysis of Data with Missing Features
Author :
DePasquale, Joseph ; Polikar, Robi
Author_Institution :
Rowan Univ., Glassboro
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
2379
Lastpage :
2384
Abstract :
We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in part by the incremental learning algorithm, Learn++. The premise is to generate an adequately large number of classifiers, each trained on a different and random combination of features, drawn from an iteratively updated distribution. To classify an instance with missing features, only those classifiers whose training data did not include the currently missing feature are used. These classifiers are combined by using a majority voting combination rule to obtain the final classification of the given instance. We had previously presented preliminary results on a similar approach, which could handle up to 10% missing data. In this study, we expand our work to include different types of rules to update the distribution, and also examine the effect of the algorithm´s primary free parameter (the number of features used to train the ensemble of classifiers) on the overall classification performance. We show that this algorithm can now accommodate up to 30% of features missing without a significant drop in performance.
Keywords :
data analysis; learning (artificial intelligence); data analysis; ensemble-of-classifiers based algorithm; incremental learning algorithm; majority voting combination rule; missing feature problem; random feature subset selection; random subspace method; Algorithm design and analysis; Clustering algorithms; Data analysis; Fuzzy neural networks; Iterative algorithms; Neural networks; Random number generation; Training data; USA Councils; Voting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371330
Filename :
4371330
Link To Document :
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