Hunting Brand Domain Forgery: A Scalable Classification for Homograph Attack

2019 ◽  
pp. 3-18 ◽  
Author(s):  
Tran Phuong Thao ◽  
Yukiko Sawaya ◽  
Hoang-Quoc Nguyen-Son ◽  
Akira Yamada ◽  
Kazumasa Omote ◽  
...  
Keyword(s):  
Scalable Classification

Scalable classification of nanoparticles: A proof of principle for process design

2019 ◽  
Vol 30 (11) ◽  
pp. 2801-2811
Author(s):  
Christina Menter ◽  
Doris Segets
Keyword(s):  
Process Design ◽  
Scalable Classification ◽  
Proof Of Principle

A Scalable Classification Algorithm for Very Large Datasets

2005 ◽  
Vol 04 (02) ◽  
pp. 83-94
Author(s):  
Dursun Delen ◽  
Marilyn G. Kletke ◽  
Jin-Hwa Kim
Keyword(s):  
Domain Knowledge ◽  
Binary Classification ◽  
Performance Testing ◽  
Classification Problem ◽  
Large Datasets ◽  
Classification Algorithm ◽  
Iterative Refinement ◽  
Classification Algorithms ◽  
Very Large Datasets ◽  
Scalable Classification

Today's organisations are collecting and storing massive amounts of data from their customer transactions and e-commerce/e-business applications. Many classification algorithms are not scalable to work effectively and efficiently with these very large datasets. This study constructs a new scalable classification algorithm (referred to in this manuscript as Iterative Refinement Algorithm, or IRA in short) that builds domain knowledge from very large datasets using an iterative inductive learning mechanism. Unlike existing algorithms that build the complete domain knowledge from a dataset all at once, IRA builds the initial domain knowledge from a subset of the available data and then iteratively improves, sharpens and polishes it using the chucks from the remaining data. Performance testing of IRA on two datasets (one with approximately five million records for a binary classification problem and another with approximately 600 K records for a seven-class classification problem) resulted in more accurate domain knowledge as compared to other prediction methods including logistic regression, discriminant analysis, neural networks, C5, CART and CHAID. Unlike other classification algorithms whose performance and accuracy deteriorate as data size increases, the efficacy of IRA improves as datasets become significantly larger.

An Information Geometry-Based Distance Between High-Dimensional Covariances for Scalable Classification

2018 ◽  
Vol 28 (10) ◽  
pp. 2449-2459
Author(s):  
Qilong Wang ◽  
Xiaoxiao Lu ◽  
Peihua Li ◽  
Zhenguo Gao ◽  
Yongri Piao
Keyword(s):  
Information Geometry ◽  
High Dimensional ◽  
Scalable Classification

scholarly journals Scalable Classification of Repetitive Time Series Through Frequencies of Local Polynomials

2015 ◽  
Vol 27 (6) ◽  
pp. 1683-1695 ◽  
Author(s):  
Josif Grabocka ◽  
Martin Wistuba ◽  
Lars Schmidt-Thieme
Keyword(s):  
Time Series ◽  
Local Polynomials ◽  
Scalable Classification
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