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BEGIN:VEVENT
DTSTAMP:20211207T055403Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211116T083000
DTEND;TZID=America/Chicago:20211116T170000
UID:submissions.supercomputing.org_SC21_sess278_rpost147@linklings.com
SUMMARY:Feature Reduction of Darshan Counters Using Evolutionary Algorithm
 s
DESCRIPTION:Posters, Research Posters\n\nFeature Reduction of Darshan Coun
 ters Using Evolutionary Algorithms\n\nRajesh, Koziol, Byna, Tang, Bez...\n
 \nFeature reduction is an integral part of data preparation in machine lea
 rning. It helps denoise the data and makes it easier to fit the model. Pre
 dicting the performance of an application using Darshan counters can be tr
 icky due to the large amount of data available, with not all of them being
  pertinent to predicting the I/O performance. There exist methods for feat
 ure reduction, the most common being recursive feature elimination (RFE). 
 The RFE method aims to correlate the features to a specific data point. We
  aim to get a subset of features that are able to distinguish between the 
 different applications, then compare the effectiveness of the subset by cr
 eating a model to predict I/O performance. We then aim to compare that wit
 h a similar model created with all the features and with a subset of featu
 res determined using RFE implemented on Scikit Learn.\n\nRegistration Cate
 gory: Tech Program Reg Pass, Exhibit Hall Only
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