Timeline for Feature selection in a "Noisy" environment

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Jul 21, 2015 at 10:21	comment	added	dcorney		Developing multiple classifiers will require more computation, but may still be the best solution. It's hard to imagine, but if you consider the (high dimensional) feature space, it may be that one region contains one target class, and another distinct region contains instances of a second class. Many classifiers are best at finding such distinct, convex regions, so multiple independent classifiers may be appropriate. By "excluding negative objects" I meant training a classifier specifically to identify (e.g.) dirt. Then the main cell classifiers don't need to even consider those objects.
Jul 20, 2015 at 13:27	comment	added	user80280		thanks for your answer. For the second question - wouldn't it be more computationally expensive to develop classifiers for each type? even so how would that address the problem of 'negative' samples? what I've started doing is designing a classifier that first decides if the given object is a cell or is not. if it is a cell then use a different classifier to decide what type of cell. but the first problem proved to be very hard. for your second suggestion - i did not understand what you meant by "exclude the negative objects leaving just the cells"? how would you go about doing that?
Jul 20, 2015 at 13:21	vote	accept	user80280
Jul 20, 2015 at 12:41	history	answered	dcorney	CC BY-SA 3.0