Treffer: AscentX: A System for Crowdsourcing and Annotating with Active Supervision.

This paper presents a system, AscentX, for labeled data creation and training with active supervision. The aim of system is that it makes collecting data and training models easier. We integrate semi-supervised learning and active learning as well as crowdsourcing learning paradigms. We design a web-based interface for labeling examples based on Python-Django and incorporate our proposed algorithms into it, including semi-supervised classification algorithms, semi-supervised clustering algorithms and with application to data filtering. Moreover, we provide the interfaces for future development including standard datasets attaching, algorithms and active learning models plunging. The system supports the visualization of labeling the examples and analyzing the results of algorithms, which is user-friendly for human annotator. We experimentally verify that AscentX achieves comparable performance to the state-of-the-art systems, using real-life (crawling web data) and benchmark datasets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)