Evolutionary Weighted Broad Learning and Its Application to Fault Diagnosis in Self-Organizing Cellular Networks

Shoufei Han, Kun Zhu, Meng Chu Zhou, Xiaojing Liu

Research output: Contribution to journalArticlepeer-review

Abstract

As a novel neural network-based learning framework, a broad learning system (BLS) has attracted much attention due to its excellent performance on regression and balanced classification problems. However, it is found to be unsuitable for imbalanced data classification problems because it treats each class in an imbalanced dataset equally. To address this issue, this work proposes a weighted BLS (WBLS) in which the weight assigned to each class depends on the number of samples in it. In order to further boost its classification performance, an improved differential evolution algorithm is proposed to automatically optimize its parameters, including the ones in BLS and newly generated weights. We first optimize the parameters with a training dataset, and then apply them to WBLS on a test dataset. The experiments on 20 imbalanced classification problems have shown that our proposed method can achieve higher classification accuracy than the other methods in terms of several widely used performance metrics. Finally, it is applied to fault diagnosis in self-organizing cellular networks to further show its applicability to industrial application problems.

Original languageEnglish (US)
JournalIEEE Transactions on Cybernetics
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Broad learning system (BLS)
  • cell outage detection
  • differential evolution (DE)
  • imbalanced classification
  • self-organizing cellular networks (SONs)
  • weighted broad learning system (WBLS)
  • wireless network fault diagnosis

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