CN111553442A - 一种分类器链标签序列的优化方法及*** - Google Patents
一种分类器链标签序列的优化方法及*** Download PDFInfo
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- CN111553442A CN111553442A CN202010397834.6A CN202010397834A CN111553442A CN 111553442 A CN111553442 A CN 111553442A CN 202010397834 A CN202010397834 A CN 202010397834A CN 111553442 A CN111553442 A CN 111553442A
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- 238000000034 method Methods 0.000 title claims abstract description 40
- 239000011159 matrix material Substances 0.000 claims abstract description 51
- 239000013598 vector Substances 0.000 claims abstract description 42
- 238000010225 co-occurrence analysis Methods 0.000 claims abstract description 11
- 230000015654 memory Effects 0.000 claims description 37
- 238000004422 calculation algorithm Methods 0.000 abstract description 35
- 238000005457 optimization Methods 0.000 abstract description 15
- 238000004891 communication Methods 0.000 description 8
- 238000012549 training Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 230000008451 emotion Effects 0.000 description 5
- 240000004808 Saccharomyces cerevisiae Species 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000007635 classification algorithm Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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- 238000011161 development Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
Description
R | l<sub>1</sub> | l<sub>2</sub> | l<sub>3</sub> | l<sub>4</sub> | l<sub>5</sub> |
l<sub>1</sub> | _ | 0.672 | 0.649 | 0.644 | 0.632 |
l<sub>2</sub> | _ | _ | 0.583 | 0.676 | 0.630 |
l<sub>3</sub> | _ | _ | _ | 0.674 | 0.619 |
l<sub>4</sub> | _ | _ | _ | _ | 0.662 |
l<sub>5</sub> | _ | _ | _ | _ | _ |
Name | Instance | Features | labels | cardinality |
emotions | 593 | 72 | 6 | 1.879 |
Enron | 1702 | 1001 | 53 | 3.378 |
Scene | 2407 | 294 | 6 | 1.074 |
Yeast | 2417 | 103 | 14 | 4.237 |
Slashdot-F | 1460 | 1079 | 22 | 1.18 |
CAL500 | 502 | 68 | 174 | 26.044 |
medical | 978 | 1449 | 45 | 1.245 |
Dataset | CC | LOCC | GCC |
yeast | 0.4585 | 0.4649 | 0.4802 |
scene | 0.5943 | 0.5938 | 0.6114 |
emotions | 0.3851 | 0.3665 | 0.3817 |
enron | 0.4034 | 0.3997 | 0.4026 |
Slashdot-F | 0.3945 | 0.4147 | 0.4050 |
CAL500 | 0.2210 | 0.2233 | 0.2347 |
medical | 0.6964 | 0.7068 | 0.7032 |
Dataset | CC | LOCC | GCC |
yeast | 0.5585 | 0.5505 | 0.5637 |
scene | 0.8547 | 0.8544 | 0.8578 |
emotions | 0.6563 | 0.6601 | 0.6598 |
enron | 0.5834 | 0.5860 | 0.5845 |
Slashdot-F | 0.6503 | 0.6538 | 0.6508 |
CAL500 | 0.5098 | 0.5104 | 0.5103 |
medical | 0.6497 | 0.6477 | 0.6497 |
Claims (9)
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112800222A (zh) * | 2021-01-26 | 2021-05-14 | 天津科技大学 | 利用共现信息的多任务辅助极限多标签短文本分类方法 |
CN113568738A (zh) * | 2021-07-02 | 2021-10-29 | 上海淇玥信息技术有限公司 | 基于多标签分类的资源分配方法、装置、电子设备及介质 |
Citations (7)
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US20160217390A1 (en) * | 2015-01-22 | 2016-07-28 | Microsoft Technology Licensing, Llc | Scalable-effort classifiers for energy-efficient machine learning |
US20180157743A1 (en) * | 2016-12-07 | 2018-06-07 | Mitsubishi Electric Research Laboratories, Inc. | Method and System for Multi-Label Classification |
CN109036577A (zh) * | 2018-07-27 | 2018-12-18 | 合肥工业大学 | 糖尿病并发症分析方法及装置 |
CN109783636A (zh) * | 2018-12-12 | 2019-05-21 | 重庆邮电大学 | 一种基于分类器链的汽车评论主题提取方法 |
CN110442707A (zh) * | 2019-06-21 | 2019-11-12 | 电子科技大学 | 一种基于seq2seq的多标签文本分类方法 |
CN110751188A (zh) * | 2019-09-26 | 2020-02-04 | 华南师范大学 | 基于多标记学习的用户标签预测方法、***及存储介质 |
US20200044938A1 (en) * | 2018-07-31 | 2020-02-06 | EMC IP Holding Company LLC | Allocation of Shared Computing Resources Using a Classifier Chain |
-
2020
- 2020-05-12 CN CN202010397834.6A patent/CN111553442B/zh active Active
Patent Citations (7)
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US20160217390A1 (en) * | 2015-01-22 | 2016-07-28 | Microsoft Technology Licensing, Llc | Scalable-effort classifiers for energy-efficient machine learning |
US20180157743A1 (en) * | 2016-12-07 | 2018-06-07 | Mitsubishi Electric Research Laboratories, Inc. | Method and System for Multi-Label Classification |
CN109036577A (zh) * | 2018-07-27 | 2018-12-18 | 合肥工业大学 | 糖尿病并发症分析方法及装置 |
US20200044938A1 (en) * | 2018-07-31 | 2020-02-06 | EMC IP Holding Company LLC | Allocation of Shared Computing Resources Using a Classifier Chain |
CN109783636A (zh) * | 2018-12-12 | 2019-05-21 | 重庆邮电大学 | 一种基于分类器链的汽车评论主题提取方法 |
CN110442707A (zh) * | 2019-06-21 | 2019-11-12 | 电子科技大学 | 一种基于seq2seq的多标签文本分类方法 |
CN110751188A (zh) * | 2019-09-26 | 2020-02-04 | 华南师范大学 | 基于多标记学习的用户标签预测方法、***及存储介质 |
Non-Patent Citations (2)
Title |
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刘各巧;郭涛;: "基于双层结构的多标签优序选择分类算法", 计算机工程与设计, no. 04, 16 April 2016 (2016-04-16) * |
蒋铭初;潘志松;尤峻;: "基于PLSA主题模型的多标记文本分类", 数据采集与处理, no. 03, 15 May 2016 (2016-05-15) * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112800222A (zh) * | 2021-01-26 | 2021-05-14 | 天津科技大学 | 利用共现信息的多任务辅助极限多标签短文本分类方法 |
CN113568738A (zh) * | 2021-07-02 | 2021-10-29 | 上海淇玥信息技术有限公司 | 基于多标签分类的资源分配方法、装置、电子设备及介质 |
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Inventor after: Zheng Rongrong Inventor after: Xue Wenting Inventor after: Zhang Qiang Inventor after: Song Bochuan Inventor after: Jia Quanye Inventor after: Chai Bo Inventor after: Zhang Wenbin Inventor before: Zhang Qiang Inventor before: Song Bochuan Inventor before: Jia Quanye Inventor before: Chai Bo |
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