Becker et al., 2023 - Google Patents
Predictive Accuracy Index in evaluating the dataset shift (case study)Becker et al., 2023
View PDF- Document ID
- 17738595176908813934
- Author
- Becker J
- Becker A
- Publication year
- Publication venue
- Procedia Computer Science
External Links
Snippet
A dataset shift takes place in a situation, where the joint distribution of inputs and outputs differs between the stages of training, testing, and using predictive models. If the distribution of current data for the implemented forecasting model changed significantly compared to the …
- 238000009826 distribution 0 abstract description 60
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0635—Risk analysis
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/0272—Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
- G06Q40/025—Credit processing or loan processing, e.g. risk analysis for mortgages
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kaya et al. | Process capability analyses based on fuzzy measurements and fuzzy control charts | |
US10600005B2 (en) | System for automatic, simultaneous feature selection and hyperparameter tuning for a machine learning model | |
Kundu et al. | Multiple failure behaviors identification and remaining useful life prediction of ball bearings | |
Du Jardin et al. | Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time | |
Comberti et al. | A combined approach for the analysis of large occupational accident databases to support accident-prevention decision making | |
Lopez-Martin et al. | Software development effort prediction of industrial projects applying a general regression neural network | |
McCarthy et al. | What do descriptive statistics tell us | |
Fadaei et al. | Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve | |
Chang et al. | Integrating in-process software defect prediction with association mining to discover defect pattern | |
Yu et al. | A framework to identify and respond to weak signals of disastrous process incidents based on FRAM and machine learning techniques | |
US11775512B2 (en) | Data analysis apparatus, method and system | |
Becker et al. | Dataset shift assessment measures in monitoring predictive models | |
Zarandi et al. | A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts | |
Aditiyawarman et al. | The study of artificial intelligent in risk-based inspection assessment and screening: A study case of inline inspection | |
Khalifa et al. | Root cause analysis of an out-of-control process using a logical analysis of data regression model and exponential weighted moving average | |
Li et al. | Operations management of critical energy infrastructure: a sustainable approach | |
JP7248188B2 (en) | Abnormality diagnosis model construction method, abnormality diagnosis method, abnormality diagnosis model construction device, and abnormality diagnosis device | |
Zaman et al. | On artificial neural networking-based process monitoring under bootstrapping using runs rules schemes | |
Becker et al. | Predictive Accuracy Index in evaluating the dataset shift (case study) | |
Terceño et al. | Prediction of business failure with fuzzy models | |
Kovářík et al. | Implementing control charts to corporate financial management | |
Dallah et al. | Outlier Detection Using the Range Distribution | |
Javed et al. | Developing Bayesian EWMA chart for change detection in the shape parameter of Inverse Gaussian process | |
Yip | Business failure prediction: a case-based reasoning approach | |
Weiß | Control charts for time-dependent categorical processes |