An Improved Hierarchical Datastructure for Nearest Neighbor Search

Nie, Mengdie; Wang, Zhijie; Gan, Chunjing; Quan, Zhe; Yao, Bin; Yin, Jian

doi:10.1609/aaai.v33i01.330110001

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 2 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, binary classification for imbalanced data has received much attention (Castro and Braga 2013; due to its wide applications in various domains such as mortality prediction, and so on (Zhao et al 2014;Bhattacharya, Rajan, and Shrivastava 2017;Liu et al 2018;Nie et al 2019;Liu et al 2019;Lin et al 2019;Xu et al ). A main characteristic of this problem is that, most of data samples belong to one class while the rest belong to the other.…”

Section: Introductionmentioning

confidence: 99%

A Novel Model for Imbalanced Data Classification

Yin¹,

Gan²,

Zhao

et al. 2020

AAAI

Self Cite

View full text Add to dashboard Cite

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.

show abstract