The Influence of Multiple Classes on Learning Online Classifiers from Imbalanced and Concept Drifting Data Streams

2022-10-15 · Agnieszka Lipska, Jerzy Stefanowski

This work is aimed at the experimental studying the influence of local data characteristics and drifts on the difficulties of learning various online classifiers from multi-class imbalanced data streams. Firstly we present a categorization of these data factors and drifts in the context of imbalanced streams, then we introduce the generators of synthetic streams that model these factors and drifts. The results of many experiments with synthetically generated data streams have shown a much greater role of the overlapping between many minority classes (the type of borderline examples) than for streams with one minority class. The presence of rare examples in the stream is the most difficult single factor. The local drift of splitting minority classes is the third influential factor. Unlike binary streams, the specialized UOB and OOB classifiers perform well enough for even high imbalance ratios. The most challenging for all classifiers are complex scenarios integrating the drifts of the identified factors simultaneously, which worsen the evaluation measures in the case of a several minority classes stronger than for binary ones. This is an extended version of the short paper presented at LIDTA'2022 workshop at ECMLPKDD2022.

📄 PDF Abstract BibTeX arXiv:2210.08359

Code (1)

dabrze/imbalanced-stream-generator 공식 구현

Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning

2013-12-23 · Luis Marujo, Anatole Gershman, Jaime Carbonell, David Martins de Matos 외

In this work, we propose two stochastic architectural models (CMC and CMC-M) with two layers of classifiers applicable to datasets with one and multiple skewed classes. This distinction becomes important when the dataset…

Classification Event Detection General Classification text-classification+1

Learning Classifiers for Imbalanced and Overlapping Data

2022-10-22 · Shivaditya Shivganesh, Nitin Narayanan N, Pranav Murali, Ajaykumar M

This study is about inducing classifiers using data that is imbalanced, with a minority class being under-represented in relation to the majority classes. The first section of this research focuses on the main characteri…

Revisiting Rumour Stance Classification: Dealing with Imbalanced Data

2020-12-01 · RDSM (COLING) 2020 12 · Yue Li, Carolina Scarton

Correctly classifying stances of replies can be significantly helpful for the automatic detection and classification of online rumours. One major challenge is that there are considerably more non-relevant replies (commen…

Classification Rumour Detection Stance Classification

A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification

2020-10-12 · Min Du, Nesime Tatbul, Brian Rivers, Akhilesh Kumar Gupta 외

Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task. Metrics such as Balanced Accuracy are commonly u…

Classification General Classification

Hybrid Ensemble optimized algorithm based on Genetic Programming for imbalanced data classification

2021-06-02 · Maliheh Roknizadeh, Hossein Monshizadeh Naeen

One of the most significant current discussions in the field of data mining is classifying imbalanced data. In recent years, several ways are proposed such as algorithm level (internal) approaches, data level (external) …