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I am really interested in learning techniques which can help me deal with the problem briefly described below.

I have a two-class {$0,1$} classification problem.

Most of the object attributes are incremental in time: when new object appears we know very little about it; as time passes by its "incremental" dynamic attributes are being updated.
Most of these "dynamic" features are incremental - for example such as

  • number of visitors of the web page
  • number of times the product was purchased etc.

Objects arrive in discrete time. Object's label is quite expensive - in the sense that it is rather difficult for expert to classify newly appeared object.

False Positive $(\widehat{y}=1, y=0)$ is VERY BAD, while False Negative $(\widehat{y}=0, y=1)$ is not so much.

Main goal is to find some classification model, which achieves good balance between

  1. model's high True Positive Rate with really low False Positive Rate and

  2. time it takes to accumulate object's dynamic attributes for its confident classification by the model.

It is crucial to classify new object $(X,Y)$ as soon as possible if its class label is $1(Y=1)$.

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Some thoughts:

  1. When training, make sure to only use variables that are stable and grows with time. That way, you will ensure that you will get weak signals on uncertain/new data (anything with ratios or percentages are out in other words).
  2. Which algorithm to use depends, as usual, a lot on the amount of data and structure of it. Anything that provides likelihood-estimates should work however, as you can just set the treshold for accepting a classification as high as you need it to be.
  3. This sounds like a problem where I would try a simple multiple regression first. They're stable, easy to understand and analyze, and it's very to cross-validate them. If avoiding False-Positives is your first and foremost concern, that would atleast be my first step. Model-performance I would focus on after creating a dependable baseline model.
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