Force selecting samples in majority class with random forest
I would suggest that you drop those 30 events and all 1's from your dataset (i presume you know which 30 they are). Then randomly select 584 samples from the remaining dataset and the stick it back together. This would give you a full dataset equally weighted that contains all 30 for definite.

See the following pseudo code (it won't run because i don't know how your identifying these 30 cases so i'm generalising)

data = import()

ones = data.select('1')

interesting = data.select('something to select the 30 data points')

data.drop('1' and 'something to select the 30 data points')

randomList = randomListGen()

selected = data.select(randomList)

joined = join(selected, ones, interesting)


As i said before this won't run on anything, if you give the language you're working in and a minimum working example then i might be able to produce something more solid.