I was wondering if there is a good source of materials about feature selection procedure?
I am not talking about PCA or anything automated. I am talking about how you choose which feature to use in your prediction model based on correlation / something else analysis + which transformation should you apply to the existing features to derive a new one, suited better for the prediction. What are current ways to do so? Is there any case studies regarding this? What materials (books / courses / articles) can you recommend about this topic?
Right now I am trying to find the best solution for kaggle bike sharing competition - http://www.kaggle.com/c/bike-sharing-demand/. This kind of a task is the most relevant to my interest, I want to learn some generic principles / workflows of feature selection that is applicable to this kind of tasks. The resulting features should be understand by a business person, not only by a automated algorithms which just somehow combine them in a weird way without any interpretation. I am not interested in automatic selection of features for image / text recognition,
Any links or thoughts are more than appreciated. Thanks.