FLSOM with Different Rates for Classification in Imbalanced Datasets

被引：0

作者：

Machon-Gonzalez, Ivan ^{[1
]}

Lopez-Garcia, Hilario ^{[1
]}

机构：

[1] Univ Oviedo, Escuela Politecn Super Ingn, Dept Ingn Elect Elect Computadores & Sistemas, Gijon Xixon 33204, Spain

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2008, PT I | 2008年 / 5163卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

There are several successful approaches dealing with imbalanced datasets. In this paper, the Fuzzy Labeled Self-Organizing Map (FLSOM) is extended to work with that type of data. The proposed approach is based on assigning two different values in the learning rate depending on the data vector membership of the class. The technique is tested with several datasets and compared with other approaches. The results seem to prove that FLSOM with different rates is a suitable tool and allows understanding and visualizing the data such as overlapped clusters.

引用

页码：642 / 651

页数：10

共 50 条

[1] To improve classification of imbalanced datasets
Shukla, Pratyusha
Bhowmick, Kiran
[J]. 2017 INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION, EMBEDDED AND COMMUNICATION SYSTEMS (ICIIECS), 2017,
[2] Classification of Antimicrobial Peptides with Imbalanced Datasets
Camacho, Francy L.
Torres, Rodrigo
Ramos Pollan, Raul
[J]. 11TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2015, 9681
[3] Discrimination Aware Classification for Imbalanced Datasets
Ristanoski, Goce
Liu, Wei
Bailey, James
[J]. PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), 2013, : 1529 - 1532
[4] Imbalanced classification in sparse and large behaviour datasets
Jellis Vanhoeyveld
David Martens
[J]. Data Mining and Knowledge Discovery, 2018, 32 : 25 - 82
[5] A robust loss function for classification with imbalanced datasets
Wang, Yidan
Yang, Liming
[J]. NEUROCOMPUTING, 2019, 331 : 40 - 49
[6] Imbalanced classification in sparse and large behaviour datasets
Vanhoeyveld, Jellis
Martens, David
[J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2018, 32 (01) : 25 - 82
[7] Categorical classifiers in multiclass classification with imbalanced datasets
Carpita, Maurizio
Golia, Silvia
[J]. STATISTICAL ANALYSIS AND DATA MINING, 2023, 16 (04) : 391 - 405
[8] ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets
Bhattacharya, Sakyajit
Rajan, Vaibhav
Shrivastava, Harsh
[J]. THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 1288 - 1294
[9] Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets
Fatourechi, Mehrdad
Ward, Rabab K.
Mason, Steven G.
Huggins, Jane
Schloegl, Alois
Birch, Gary E.
[J]. SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, 2008, : 777 - +
[10] Deep Learning Applied to Imbalanced Malware Datasets Classification
Salas, Marcelo Palma
de Geus, Paulo Licio
[J]. JOURNAL OF INTERNET SERVICES AND APPLICATIONS, 2024, 15 (01) : 342 - 359

← 1 2 3 4 5 →