Evaluation of Intelligent Information System

被引：0

作者：

Liu, Wenhong ^{[1
]}

Li, Yong ^{[2
]}

Huang, Bo ^{[2
]}

机构：

[1] Beijing Inst Tracking & Telecommun Technol, Beijing, Peoples R China

[2] Chengdu Guoxinan Informat Ind Base Co Ltd, Chengdu, Sichuan, Peoples R China

来源：

2022 IEEE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY COMPANION, QRS-C | 2022年

关键词：

Intelligent information system; artificial intelligence; data quality; model quality; evaluation study;

D O I：

10.1109/QRS-C57518.2022.00035

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

While artificial intelligence evolves rapidly, intelligent information systems are making significant progress. The core of intelligent information systems is data and models. As a result, data quality and model quality are significant for ensuring the quality of intelligent information systems. However, traditional software testing methods can not adequately evaluate the quality of data and models. In this paper, we propose the data quality evaluation method and the model evaluation method. The experiment results show the two methods are effective.

引用

页码：183 / 188

页数：6

共 10 条

[1] Random forests
Breiman, L
[J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
[2] Delashmit WH, 2005, 7 ANN MEMPH AR ENG S
[3] DISCRIMINATORY ANALYSIS - NONPARAMETRIC DISCRIMINATION - CONSISTENCY PROPERTIES
FIX, E
HODGES, JL
[J]. INTERNATIONAL STATISTICAL REVIEW, 1989, 57 (03) : 238 - 247
[4] Stochastic gradient boosting
Friedman, JH
[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2002, 38 (04) : 367 - 378
[5] ROBUST ESTIMATION OF LOCATION PARAMETER
HUBER, PJ
[J]. ANNALS OF MATHEMATICAL STATISTICS, 1964, 35 (01): : 73 - &
[6] Suthaharan S, 2016, INTEGR SER INFORM SY, V36, P237, DOI 10.1007/978-1-4899-7641-3_10
[7] Logistic Regression Relating Patient Characteristics to Outcomes
Tolles, Juliana
Meurer, William J.
[J]. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2016, 316 (05): : 533 - 534
[8] Zhu H, 2019, ADV NEUR IN, V32
[9] Learning Tree-based Deep Model for Recommender Systems
Zhu, Han
Li, Xiang
Zhang, Pengye
Li, Guozheng
He, Jie
Li, Han
Gai, Kun
[J]. KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2018, : 1079 - 1088
[10] Zhuo Jingwei, 2020, INT C MACHINE LEARNI, P11650

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