Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy

11/02/2021
by   Fan Li, et al.
0

Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides, the existing methods are likely to generate noisy instances. To solve these problems, this paper proposes a deep instance envelope network-based imbalanced learning algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This algorithm can guarantee high quality balanced instances using a deep instance envelope network in the absence of prior knowledge. In the experimental section, thirty-three popular public datasets are used for verification, and over ten representative algorithms are used for comparison. The experimental results show that the proposed approach significantly outperforms other popular methods.

READ FULL TEXT
research
11/02/2017

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Learning from class-imbalanced data continues to be a common and challen...
research
10/10/2021

Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets

Time Series Classification (TSC) has drawn a lot of attention in literat...
research
06/25/2022

Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency

The class imbalance problem is important and challenging. Ensemble appro...
research
10/09/2022

An Instance Selection Algorithm for Big Data in High imbalanced datasets based on LSH

Training of Machine Learning (ML) models in real contexts often deals wi...
research
07/11/2018

Instance-based entropy fuzzy support vector machine for imbalanced data

Imbalanced classification has been a major challenge for machine learnin...
research
11/17/2021

Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data

Parkinson disease (PD)'s speech recognition is an effective way for its ...
research
03/21/2022

ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets

Nowadays, many industries have applied classification algorithms to help...

Please sign up or login with your details

Forgot password? Click here to reset