IEBench: Benchmarking Streaming Learners on Imbalanced Evolving Data Streams

Nowadays, data coming from electronic devices surround us. Analyzing them all in real-time, also considering the class imbalance and concept drift challenges is a big challenge and the starting point to understand where to innovate. This paper proposes IEBench, an easy-to-use benchmarking environmen...

Full description

Saved in:
Bibliographic Details
Main Authors: Bernardo, Alessio, Ziffer, Giacomo, Valle, Emanuele Della
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Benchmarking Environment
Buildings
Class Imbalance
Concept Drift
Conferences
Data models
Machine learning algorithms
Particle measurements
Real-time systems
Streaming Machine Learning
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Nowadays, data coming from electronic devices surround us. Analyzing them all in real-time, also considering the class imbalance and concept drift challenges is a big challenge and the starting point to understand where to innovate. This paper proposes IEBench, an easy-to-use benchmarking environment for comparing streaming learners. We implemented various state-of-art algorithms, and we conducted a comprehensive experimental campaign with IEBench. We evaluated the algorithms on artificial and real data streams with different imbalance levels and concept drift types. We collected empirical evidence of the role and impact of existing methods for rebalancing data streams in improving performance during different types of concept drift. IEBench eases the practitioners' task of testing existing algorithms on a new data stream and the scientists' one of developing new algorithms and systematically comparing them with the state-of-the-art.
ISSN:2375-9259
DOI:10.1109/ICDMW53433.2021.00048