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Abstract

The main goal of Active Learning (AL), is to empower the learning process by reducing the cost of obtaining the labels with a limited training samples and by selecting the most informative samples from the unlabelled set. AL was implemented to solve a wide range of problems in all fields [1]. In this paper, we will go forward to utilize a multiclass active learning model over an ecological zones dataset in order to classify the dune-beach interface to a divided sub-environments category. Pool, ranked & stream-based sampling were used as an active learning frames where three query strategies were tested with each frame to achieve the best performance. The performance metrics values would be illustrated in three different comparative statements, in term of different framework implemented by different query strategies, in term of incremental learning process behaviour in a pool-based sampling frame work by selecting 20 different query quires from the unlabelled set to present the incremental learning process behaviour with each query strategy (random, entropy & margin) and finally in term of incremental learning process behaviour above a three different AL frame works and three different query strategies in term of accuracy metric.