Modified the Cliff and morph code to adjust binary values. They work for continuous values. Now the regions for 0 is randomly picked between (0 - 0.5) and for 1, it is picked from (0.5 - 1)
Sharing from UCI, so training available from that and tested on 6 companies. They are 'AOL', 'Facebook', 'PayPal', 'Google','Apple', 'Yahoo'.
0.3 cliff
Baseline:
Keeping 0.3 for UCI training without morphing and compared against those 6 companies.
Results:
Lace rbf kernel svm performs better in most cases
Surprising in some cases LACE performed better in few datasets with few learners. With others, it didnt change much. So LACE either performed better or stayed constant.
Experiment:
Baseline:
Results:
AOL
Apple
Facebook
Google
Paypal
Yahoo