Lu et al., 2020 - Google Patents
A data-driven framework for identifying important components in complex systemsLu et al., 2020
View PDF- Document ID
- 3999531739932475357
- Author
- Lu X
- Baraldi P
- Zio E
- Publication year
- Publication venue
- Reliability Engineering & System Safety
External Links
Snippet
Complex technical infrastructures are systems of systems characterized by hierarchical structures, made by thousands of mutually interconnected components performing different functions. Given their complexity, it is difficult to derive their functional logic using traditional …
- 238000007637 random forest analysis 0 abstract description 54
Classifications
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
-
- 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
- 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
- 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
- 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
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6228—Selecting the most significant subset of features
-
- 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
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lee et al. | A data-driven approach to selection of critical process steps in the semiconductor manufacturing process considering missing and imbalanced data | |
| CN106844194B (en) | A kind of construction method of multi-level software fault diagnosis expert system | |
| CN104820716B (en) | Equipment Reliability appraisal procedure based on data mining | |
| Lu et al. | A data-driven framework for identifying important components in complex systems | |
| Kulkarni et al. | Weka powerful tool in data mining | |
| Wang et al. | Research on anomaly detection and real-time reliability evaluation with the log of cloud platform | |
| Tsoukalas et al. | Machine learning for technical debt identification | |
| CN116451142A (en) | Water quality sensor fault detection method based on machine learning algorithm | |
| CN119557607B (en) | Data tracing method and system based on big data and blockchain multidimensional features | |
| CN116578980A (en) | Code analysis method and device based on neural network and electronic equipment | |
| Bellisario et al. | Contributions of MIR to soundscape ecology. Part 3: Tagging and classifying audio features using a multi-labeling k-nearest neighbor approach | |
| CN114547606B (en) | Mobile Internet operating system third-party application risk analysis method and system | |
| Ardimento et al. | Transfer Learning for Just-in-Time Design Smells Prediction using Temporal Convolutional Networks. | |
| Gao et al. | The use of ensemble-based data preprocessing techniques for software defect prediction | |
| CN117389878A (en) | Software defect prediction method and system based on correlation coefficient and mutual information weighting | |
| Wu et al. | Data preprocessing and data parsimony in corporate failure forecast models: evidence from Australian materials industry | |
| Harikiran et al. | Software Defect Prediction Based Ensemble Approach. | |
| Matcha et al. | Using Deep Learning Classifiers to Identify Candidate Classes for Unit Testing in Object-Oriented Systems. | |
| Belete et al. | A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS Dataset | |
| Chitsazian et al. | Detecting Concept Drift for the reliability prediction of Software Defects using Instance Interpretation | |
| Naufal et al. | Software defect detection based on selected complexity metrics using fuzzy association rule mining and defective module oversampling | |
| Gaol et al. | Software testing model by measuring the level of accuracy fault output using neural network algorithm | |
| CN119808794B (en) | A big data intelligent analysis method and system based on AI | |
| Lerman et al. | A new probabilistic measure of interestingness for association rules, based on the likelihood of the link | |
| CN119210812B (en) | A method for mining covert attack behavior |