SG11201809890PA - Method of training random forest model, electronic device and storage medium - Google Patents
Method of training random forest model, electronic device and storage mediumInfo
- Publication number
- SG11201809890PA SG11201809890PA SG11201809890PA SG11201809890PA SG11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA SG 11201809890P A SG11201809890P A SG 11201809890PA
- Authority
- SG
- Singapore
- Prior art keywords
- training
- random forest
- forest model
- model
- electronic device
- Prior art date
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Human Resources & Organizations (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Game Theory and Decision Science (AREA)
- Medical Informatics (AREA)
- Computational Linguistics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
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Abstract
METHOD OF TRAINING RANDOM FOREST MODEL, ELECTRONIC DEVICE AND STORAGE MEDIUM The disclosure relates to a method of training a random forest model, an electronic device and a storage medium. The method of training the random forest model includes: analyzing, by a system of controlling model training, whether model training conditions are met or not; if the model training conditions are met, determining whether reconstructive training needs to be carried out on the random forest model or not; if the reconstructive training needs to be carried out on the random forest model, carrying out the reconstructive training on the random forest model by using sample data; if the reconstructive training does not need to be carried out on the random forest model, carrying out corrective training on the random forest model by using the sample data. The disclosure can reduce the number of times of training on the random forest model, relieve the system burden and improve the system efficiency. Fig. 2
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710147698.3A CN107632995B (en) | 2017-03-13 | 2017-03-13 | The method and model training control system of Random Forest model training |
PCT/CN2017/091362 WO2018166113A1 (en) | 2017-03-13 | 2017-06-30 | Random forest model training method, electronic apparatus and storage medium |
Publications (1)
Publication Number | Publication Date |
---|---|
SG11201809890PA true SG11201809890PA (en) | 2018-12-28 |
Family
ID=61099137
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
SG11201809890PA SG11201809890PA (en) | 2017-03-13 | 2017-06-30 | Method of training random forest model, electronic device and storage medium |
Country Status (8)
Country | Link |
---|---|
US (1) | US20210081847A1 (en) |
EP (1) | EP3413212A4 (en) |
JP (1) | JP6587330B2 (en) |
KR (1) | KR102201919B1 (en) |
CN (1) | CN107632995B (en) |
AU (1) | AU2017404119A1 (en) |
SG (1) | SG11201809890PA (en) |
WO (1) | WO2018166113A1 (en) |
Families Citing this family (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109377388B (en) * | 2018-09-13 | 2023-08-18 | 深圳平安医疗健康科技服务有限公司 | Medical insurance application method, medical insurance application device, computer equipment and storage medium |
US11625640B2 (en) * | 2018-10-05 | 2023-04-11 | Cisco Technology, Inc. | Distributed random forest training with a predictor trained to balance tasks |
CN109886544A (en) * | 2019-01-17 | 2019-06-14 | 新奥数能科技有限公司 | Construct method, apparatus, medium and the electronic equipment of energy efficiency of equipment curve model |
CN110175677A (en) * | 2019-04-16 | 2019-08-27 | 平安普惠企业管理有限公司 | Automatic update method, device, computer equipment and storage medium |
CN110070128A (en) * | 2019-04-22 | 2019-07-30 | 深圳市绘云生物科技有限公司 | A kind of chronic liver disease risk evaluating system based on Random Forest model |
CN110532445A (en) * | 2019-04-26 | 2019-12-03 | 长佳智能股份有限公司 | The cloud transaction system and its method of neural network training pattern are provided |
CN110232154B (en) * | 2019-05-30 | 2023-06-09 | 平安科技(深圳)有限公司 | Random forest-based product recommendation method, device and medium |
KR102223161B1 (en) * | 2019-10-11 | 2021-03-03 | 노주현 | System and methods of predicting timeliness products based on weather data |
KR20210050362A (en) | 2019-10-28 | 2021-05-07 | 주식회사 모비스 | Ensemble pruning method, ensemble model generation method for identifying programmable nucleases and apparatus for the same |
CN111091408A (en) * | 2019-10-30 | 2020-05-01 | 北京天元创新科技有限公司 | User identification model creating method and device and identification method and device |
KR102092684B1 (en) * | 2020-01-23 | 2020-03-24 | 주식회사 두두아이티 | Apparatus and method for cyber security training based on random forest |
CN111767958A (en) * | 2020-07-01 | 2020-10-13 | 武汉楚精灵医疗科技有限公司 | Real-time enteroscopy withdrawal time monitoring method based on random forest algorithm |
EP4216113A4 (en) * | 2020-09-18 | 2024-06-05 | Nippon Telegraph And Telephone Corporation | ASSESSMENT DEVICE, ASSESSMENT PROCEDURE AND ASSESSMENT PROGRAM |
CN113466713B (en) * | 2021-07-15 | 2024-04-12 | 北京工业大学 | Lithium battery safety degree estimation method and device based on random forest |
CN116759014B (en) * | 2023-08-21 | 2023-11-03 | 启思半导体(杭州)有限责任公司 | Random forest-based gas type and concentration prediction method, system and device |
Family Cites Families (20)
Publication number | Priority date | Publication date | Assignee | Title |
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US20080133434A1 (en) | 2004-11-12 | 2008-06-05 | Adnan Asar | Method and apparatus for predictive modeling & analysis for knowledge discovery |
CN102221655B (en) * | 2011-06-16 | 2013-08-07 | 河南省电力公司济源供电公司 | Random-forest-model-based power transformer fault diagnosis method |
EP2562690A3 (en) * | 2011-08-22 | 2014-01-22 | Siemens Aktiengesellschaft | Assigning a number of reference measurement data sets to an input measurement data set |
CN102508907B (en) * | 2011-11-11 | 2013-11-20 | 北京航空航天大学 | Dynamic recommendation method based on training set optimization for recommendation system |
JP5953151B2 (en) * | 2012-07-13 | 2016-07-20 | 日本放送協会 | Learning device and program |
JP5946364B2 (en) | 2012-08-21 | 2016-07-06 | 株式会社Nttドコモ | Time-series data processing system and time-series data processing method |
WO2014053017A1 (en) * | 2012-10-03 | 2014-04-10 | Iselect Ltd | Systems and methods for use in marketing |
CA2905072A1 (en) | 2013-03-15 | 2014-09-25 | The Cleveland Clinic Foundation | Self-evolving predictive model |
WO2015134665A1 (en) * | 2014-03-04 | 2015-09-11 | SignalSense, Inc. | Classifying data with deep learning neural records incrementally refined through expert input |
CN104155596B (en) * | 2014-08-12 | 2017-01-18 | 北京航空航天大学 | Artificial circuit fault diagnosis system based on random forest |
US9836701B2 (en) * | 2014-08-13 | 2017-12-05 | Microsoft Technology Licensing, Llc | Distributed stage-wise parallel machine learning |
CN105718490A (en) * | 2014-12-04 | 2016-06-29 | 阿里巴巴集团控股有限公司 | Method and device for updating classifying model |
CN105809707B (en) * | 2014-12-30 | 2018-11-27 | 江苏慧眼数据科技股份有限公司 | A kind of pedestrian tracting method based on random forests algorithm |
CN106156809A (en) * | 2015-04-24 | 2016-11-23 | 阿里巴巴集团控股有限公司 | For updating the method and device of disaggregated model |
US20160350675A1 (en) * | 2015-06-01 | 2016-12-01 | Facebook, Inc. | Systems and methods to identify objectionable content |
CN105045819B (en) * | 2015-06-26 | 2018-04-20 | 深圳市腾讯计算机系统有限公司 | The model training method and device of a kind of training data |
CN105678567A (en) * | 2015-12-31 | 2016-06-15 | 宁波领视信息科技有限公司 | Accurate prediction system based on big data deep learning |
CN105844300A (en) * | 2016-03-24 | 2016-08-10 | 河南师范大学 | Optimized classification method and optimized classification device based on random forest algorithm |
CN105912500B (en) * | 2016-03-30 | 2017-11-14 | 百度在线网络技术(北京)有限公司 | Machine learning model generation method and device |
CN105931224A (en) * | 2016-04-14 | 2016-09-07 | 浙江大学 | Pathology identification method for routine scan CT image of liver based on random forests |
-
2017
- 2017-03-13 CN CN201710147698.3A patent/CN107632995B/en active Active
- 2017-06-30 SG SG11201809890PA patent/SG11201809890PA/en unknown
- 2017-06-30 KR KR1020187017282A patent/KR102201919B1/en active Active
- 2017-06-30 WO PCT/CN2017/091362 patent/WO2018166113A1/en active Application Filing
- 2017-06-30 JP JP2018530893A patent/JP6587330B2/en active Active
- 2017-06-30 EP EP17897210.5A patent/EP3413212A4/en not_active Ceased
- 2017-06-30 US US16/084,232 patent/US20210081847A1/en not_active Abandoned
- 2017-06-30 AU AU2017404119A patent/AU2017404119A1/en not_active Abandoned
Also Published As
Publication number | Publication date |
---|---|
KR102201919B1 (en) | 2021-01-12 |
CN107632995B (en) | 2018-09-11 |
EP3413212A1 (en) | 2018-12-12 |
AU2017404119A9 (en) | 2019-06-06 |
KR20190022431A (en) | 2019-03-06 |
AU2017404119A1 (en) | 2018-10-11 |
JP2019513246A (en) | 2019-05-23 |
EP3413212A4 (en) | 2019-04-03 |
WO2018166113A1 (en) | 2018-09-20 |
CN107632995A (en) | 2018-01-26 |
JP6587330B2 (en) | 2019-10-09 |
US20210081847A1 (en) | 2021-03-18 |
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