# Sentence classification with SVM

I'm trying to solve the following problem, I want to classify each row of a particular machine log to output only interesting information such as relevant/non-relevant.I have collected a dataset from such logs and I created a bag of words with ~11000 features.I'm trying to figure out what would be the best approach here to do exactly this classification.I was thinking of using SVM because as far as I know it handles high dimensional input pretty well, but since my feature vector would look like 3-4 positive values and the rest 11000 feautures set to 0 I doubt it will work well.

//Here's an example of 3 rows from my dataset

Notification Minor FPGA Status hw_node:0 Added Node: 1 // relevant
Notification Minor Battery failed test hw_node:1,hw_battery:0 Auto-resolve NEMOE event by Sysmgr at INIT  //relevant
Notification Minor CLI command error sw_cli {3paradm super all {{0 8}} -1 172.16.30.60 10741} {Command: startprog 0 ifconfig fcnet2 Error: user permission denied} {} // non-relevant


I intent to scan each word in the row and create a feature vector which I would pass to the SVM for classification.

My question would be - is this a good way to handle such problem ? I still haven't tested anything yet as I'm quite limited to computational resources at the moment and each test would take a while to complete..

• @shf8888, did you mean a formula likes $\left(0.5 + 0.5 \frac{tf}{\max tf}\right) \times \ln\frac{N}{n}$? – Nick Jul 6 '17 at 1:03