Random Subspace Feature Selection for Analysis of Data with Missing Features

1. Random Subspace Feature Selection for Analysis of Data with Missing Features Presented by: Joseph DePasquale Student Activities Conference 2007 This material is based upon work supported by the National Science Foundation under Grant No ECS-0239090. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

2. Outline • Motivation • Missing feature algorithm • Selecting features for training • Finding usable classifiers for testing • Impact of free parameters • Number of features used for training • Distribution update parameter β

3. Motivation • Missing data is a real world issue • Failed equipment • Human error • Natural phenomena • Matrix multiplication can not be used if a single data value is left out Missing Feature

4. Training

6. Training Usable Classifiers fi Ci X Feature used in training Usable classifier Feature not used in training

7. Experimental Setup • Research has been done for static selection of features used for training

8. Volatile Organic Compound Database

9. Pen Digits Recognition Database

10. Ionosphere Database

11. Wisconsin Breast Cancer Database

12. Conclusions • β does not significantly impact the algorithm, the number of features used for training does have an impact

13. References [1]Hussein, S., “Random feature subspace ensemble based approaches for the analysis of data with missing features,” Submitted Spring 2006. [2] Haykin, S., “Neural Networks A Comprehensive Foundation,” New Jersey: Prentice Hall, 1999. [3] “UCI repository,” [Online Document], Accessed: 25 Nov 2006. http://www.ics.uci.edu/~mlearn/MLRepository.html

14. Learn++.MF • Training • Selecting features from distribution • Training the network • Update likelihood of selecting features • Testing • Data corruption • Identify usable classifiers • Simulation