I have came across a binary classification problem which I dont think falls under traditional binary classification. So just wanted to share with you guys to get some more ideas.
I have a series of data records which is in a chronological order. For simplicity lets say I have 100 data records and I have a target value as 1 or 0 for every consecutive 10 data values.
e.g. data = [1,2,3,4,5,6,7,8,9,10]
Training data would look like this:
[1,2,3] => 1, [2,3,4] => 0, [3,4,5] => 0, [4,5,6] => 0, [5,6,7] => 1 and so on and so forth. please note that order of values does matters
I am currently struggling on how to handle this idea. Is there any Machine learning tools that can handle this? like Amazon Machine Learning or BigML etc.
I am thinking of using some kind of hash function that can hash the series to specific value and that value can be used for one to one binary classification. However I am not sure if I am going in the right direction.
EDIT: More precisely I have a data consisting of 5 numerical features. Each instance of these 5 feature values are coming in a timely manner like every second. This instance or vector of 5 feature values independent of each other does not have any a significance however when consider in a group they have (In this case group is consecutive 100 vectors). These groups can be labeled as either 1 or 0 based on their characteristics. Now I wanted to build a classifier for this that can in future classify the data series of 100 instance as 1 or 0.
Any help would be appreciated.