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Proactive Learning Algorithms: A Survey of the State of the Art and Implementation of Novel and Concrete Algorithm for (Unstructured) Data Classification


Anis,  Myriam
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Anis, M. (2019). Proactive Learning Algorithms: A Survey of the State of the Art and Implementation of Novel and Concrete Algorithm for (Unstructured) Data Classification. Master Thesis, Universität des Saarlandes, Saarbrücken.

Cite as: http://hdl.handle.net/21.11116/0000-0005-9C5B-6
Artificial Intelligence (AI) has become one of the most researched fields nowadays. Ma- chine Learning (ML) is one of the most popular AI domains, where systems are created with the capability of automatic learning and improving from the learning experience. The current revolution in the size and cost of electronic storage allows for the existence of enormous amount of data that can be used for ML training. Unfortunately, not all of this data is labelled. The process of manually labelling documents can be expen- sive, time consuming and subject to human errors. Active Learning (AL) addresses this challenge by finding a sample of the enormous data corpus that, if labelled, can substitute the use of the whole dataset. AL routes this sample to a human labeller to formulate the training dataset needed for the ML model. AL assumes that there exists a single, infallible and indefatigable labeller. These assumptions cannot cope to real world problems. The main focus of this work is to introduce Proactive Learning (PL) to an existing AL system. PL aims at generalizing the problem, solved by AL, by relaxing all of its assumptions about the user. The main addition of this project is enhancing automatic text classification by combining knowledge from the domain of PL and from Instance Relabelling paradigms to update the currently implemented AL system. The implemented PL system is tested on the 20 Newsgroups, Reuters and AG News datasets. The system is capable of reaching impressive results in detecting and predicting users actions, which allows the system to efficiently route labelling tasks to the best users, leading to minimize the risk of receiving wrong labels.