• Publications
  • Influence
Learning from Imbalanced Data
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance theExpand
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ADASYN: Adaptive synthetic sampling approach for imbalanced learning
This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minorityExpand
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 Abstract—Power amplifier (PA) models, such as the neural network (NN) models and the multilayer NN models, have the problems of high complexity. In this paper, we firstly propose a novel behaviorExpand
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SERA: Selectively recursive approach towards nonstationary imbalanced stream data mining
  • S. Chen, Haibo He
  • Computer Science
  • International Joint Conference on Neural Networks
  • 14 June 2009
Recent years have witnessed an incredibly increasing interest in the topic of stream data mining. Despite the great success having been achieved, current approaches generally assume that the classExpand
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RAMOBoost: Ranked Minority Oversampling in Boosting
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imbalanced data.Expand
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A three-network architecture for on-line learning and optimization based on adaptive dynamic programming
In this paper, we propose a novel adaptive dynamic programming (ADP) architecture with three networks, an action network, a critic network, and a reference network, to develop internalExpand
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Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach
Difficulties of learning from nonstationary data stream are generally twofold. First, dynamically structured learning framework is required to catch up with the evolution of unstable class concepts,Expand
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ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]
  • Bo Tang, Haibo He
  • Computer Science
  • IEEE Computational Intelligence Magazine
  • 16 July 2015
This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike theExpand
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A local density-based approach for outlier detection
A local density-based approach for outlier detection is proposed.The theoretical properties of the proposed outlierness score are derived.Three types of nearest neighbors are presented. This paperExpand
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A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities
The ubiquitous deployment of various kinds of sensors in smart cities requires a new computing paradigm to support Internet of Things (IoT) services and applications, and big data analysis. FogExpand
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