"I've tested it on the data from the KDD 2006 cup a contest in the KDD conference whose goal was to identify Pulmonary Embolism based on data generated from CT scans and it out scored the cup winners by a 50% margin."
There are a lot of different fields in ML. I don't believe that you have an algorithm that beats all algorithms out there , even if those are specialized on a specific setting.
You are right there are quite a few problem types in ML and a lot of different algorithms but because my idea is a basic insight into something that is missing in existing algorithms, I've been able to incorporate the change into several different algorithms.
Can't really get you a score that you'll be able to trust without submitting a result set, can't submit a result set without agreeing to publicize my algorithm...
Pick a different test. One where you can do the verification without a 3rd party that requires me to relinquish my trade secret.
That's not true. You can submit your results and then refuse to release your algorithm, disqualifying you from the competition.
"Upon qualifying, as described above, the Participant is required to submit within one (1) week for judging a description of their algorithm along with all source code. The Participant warrants that the source code is either fully or substantially developed and functions or will function as represented by the description. Failure to deliver both the description and source code within one (1) week will disqualify that entry and additional qualifying entries will be considered."
Ok missed that part in the contract... it was a bit long and in legalese.
I'll tinker with it to fit it to the problem (different algorithm category, mine is best fitted for classification problems and the test is a clustering problem. The dataset needs to be manipulated a bit first for it to work )
