Evolving text stream classification with a novel neural ensemble architecture

Date

2022-01

Editor(s)

Advisor

Can, Fazlı

Supervisor

Co-Advisor

Co-Supervisor

Instructor

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Abstract

We study on-the-fly classification of evolving text streams in which the relation between the input data target labels changes over time—i.e. “concept drift”. These variations decrease the model’s performance, as predictions become less accurate over-time and they necessitate a more adaptable system. We introduce Adaptive Neural Ensemble Network (AdaNEN ), a novel ensemble-based neural approach, capable of handling concept drift in text streams. With our novel architecture, we address some of the problems neural models face when exploited for online adaptive learning environments. The problem of evolving text stream classification is relatively unexplored and most existing studies address concept drift detection and handling in numerical streams. We hypothesize that the lack of public and large-scale experimental data could be one reason. To this end, we propose a method based on an existing approach for generating evolving text streams by inducing various types of concept drifts to real-world text datasets. We provide an extensive evaluation of our proposed approach using 12 stateof- the-art baselines and eight datasets. Our experimental results show that our proposed method, AdaNEN, consistently outperforms the existing approaches in terms of predictive performance with conservative efficiency.

Source Title

Publisher

Course

Other identifiers

Book Title

Degree Discipline

Computer Engineering

Degree Level

Master's

Degree Name

MS (Master of Science)

Citation

Published Version (Please cite this version)

Language

English

Type