Incremental Learning for Large Scale Classification Systems

被引：4

作者：

Alexopoulos, Athanasios ^{[1
]}

Kanavos, Andreas ^{[1
,2
]}

Giotopoulos, Konstantinos ^{[2
]}

Mohasseb, Alaa ^{[3
]}

Bader-El-Den, Mohamed ^{[3
]}

Tsakalidis, Athanasios ^{[1
]}

机构：

[1] Univ Patras, Comp Engn & Informat Dept, Patras, Greece

[2] Technol Educ Inst Western Greece, Patras, Greece

[3] Univ Portsmouth, Sch Comp, Portsmouth, Hants, England

来源：

ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2018 | 2018年 / 520卷

关键词：

Apache Spark; Apache MLlib; Big data; Classification; Computing performance; DataFrame; Spark SQL; SPARK;

D O I：

10.1007/978-3-319-92016-0_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the main characteristics of our time is the growth of the data volumes. We collect data literally from everywhere; smart phones, smart devices, social media and the health care system, which defines a small portion of the sources of the big data. The big data growth poses two main difficulties, storing and processing them. For the former, there are certain new technologies that enable us to store large amounts of data in a fast and reliable way. For the latter, new application frameworks have been developed. In this paper, we perform classification analysis using Apache Spark in one real dataset. The classification algorithms that we have used are multiclass, and we are going to examine the effect of the dataset size and input features on the classification results.

引用

页码：112 / 122

页数：11

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