Authors:
Sana Ben Hamida
1
and
Ghita Benjelloun
2
Affiliations:
1
Paris Dauphine University, PSL Research University, CNRS, UMR7243, LAMSADE, 75016 Paris, France
;
2
Paris Dauphine University, PSL Research University, 75016 Paris, France
Keyword(s):
Genetic Programming, DEAP, Active Learning, Random Sampling, Weighted Sampling, Occupancy Detection, Pulsar Detection.
Abstract:
Complexity, variety and large sizes of data bases make the Knowledge extraction a difficult task for supervised machine learning techniques. It is important to provide these techniques additional tools to improve their efficiency when dealing with such data. A promising strategy is to reduce the size of the training sample seen by the learner and to change it regularly along the learning process. Such strategy known as active learning, is suitable for iterative learning algorithms such as Evolutionary Algorithms. This paper presents some sampling techniques for active learning and how they can be applied in a hierarchical way. Then, it details how these techniques could be implemented into DEAP, a Python framework for Evolutionary Algorithms. A comparative study demonstrates how active learning improve the evolutionary learning on two data bases for detecting pulsars and occupancy in buildings.