GET A LIST OF FEATURE EXTRACTORS BASED ON FEATURE IMPORTANCE TECHNIQUES

Abstract

It is well-known, we live in the digital industrial era and we have a lot of accumulated volumes of structured and unstructured data. The accumulated data, actually, can help us make our solutions more efficient in different areas of human activities. That is why we are trying to recognize which kind of data can resolve which kind of problem. For this purpose, we have a lot of useful techniques for assessment of the data feature importance.

We are more confident in feature importance techniques when we work with numerical values of structured data because the detected objects are described by particular values. Alas, when extracting the features from unstructured data, like from images, it is still a question of how feature importance techniques are useful because it is either a question of how we are going to describe a detected object or which set of features might be an optimal one.

Of course, it is a good case when we can extract a lot of different features of a detected object but how it is enough if we want to recognize it from a list of other possible objects? This problem we are going to resolve this by an approach where we describe the nature of the detected object via its unique features either than feature importance techniques.

In the article, we consider the state of art feature importance methods, feature extraction algorithms and methodology of how to create the optimal set of feature extractors.

Keywords: Computer vision, object detection, feature importance, machine learning, water pollution, liquid contamination.