A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off

Abstract

Recently, active learning is considered a promising approach for data acquisition due to the significant cost of the data labeling process in many real world applications, such as natural language processing and image processing. Most active learning methods are merely designed to enhance the learning model accuracy. However, the model accuracy may not be the primary goal and there could be other domain-specific objectives to be optimized. In this work, we develop a novel active learning framework that aims to solve a general class of optimization problems. The proposed framework mainly targets the optimization problems exposed to the exploration-exploitation trade-off. The active learning framework is comprehensive, it includes exploration-based, exploitation-based and balancing strategies that seek to achieve the balance between exploration and exploitation. The paper mainly considers regression tasks, as they are under-researched in the active learning field compared to classification tasks. Furthermore, in this work, we investigate the different active querying approaches—pool-based and the query synthesis—and compare them. We apply the proposed framework to the problem of learning the price-demand function, an application that is important in optimal product pricing and dynamic (or time-varying) pricing. In our experiments, we provide a comparative study including the proposed framework strategies and some other baselines. The accomplished results demonstrate a significant performance for the proposed methods.

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

“…Finally, by combining schemes from the fields of active regression learning [ 65 , 66 ] and semi-supervised regression [ 53 ] along with the proposed classification algorithm, a general combination scheme could be put forward that would be able to handle numeric and categorical targets.…”

Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing With Demand Learning

“…Recently, active learning has been an effective paradigm whenever the cost of data collection is substantial [60]. In [61], the authors develop an active learning framework that aims to balance the exploration-exploitation trade-off in optimization problems. They apply their framework to the dynamic pricing with demand learning problem.…”