Lee et al., 2013 - Google Patents
Automatic selection of classification learning algorithms for data mining practitionersLee et al., 2013
- Document ID
- 12551961264767670143
- Author
- Lee J
- Giraud-Carrier C
- Publication year
- Publication venue
- Intelligent Data Analysis
External Links
Snippet
Classification algorithm selection is an open research problem whose solution has tremendous value for practitioners. In recent years, metalearning has emerged as a viable approach. Unfortunately, the ratio of examples to classes is small at the metalevel for any …
- 238000007418 data mining 0 title description 8
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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- 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
- 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
- G06F17/30595—Relational databases
- G06F17/30598—Clustering or classification
-
- 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
- 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
- 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
-
- 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
-
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lin et al. | A survey on reinforcement learning for recommender systems | |
| AlNuaimi et al. | Streaming feature selection algorithms for big data: A survey | |
| Gasparetti et al. | Community detection in social recommender systems: a survey | |
| Arrar et al. | A comprehensive survey of link prediction methods: D. Arrar et al. | |
| Wang et al. | Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series | |
| Doquire et al. | Feature selection with missing data using mutual information estimators | |
| Lee et al. | Automatic selection of classification learning algorithms for data mining practitioners | |
| Chen et al. | A rough set approach to feature selection based on ant colony optimization | |
| De La Iglesia | Evolutionary computation for feature selection in classification problems | |
| Choudhury et al. | Time-aware link prediction to explore network effects on temporal knowledge evolution | |
| Kang et al. | A retrieval strategy for case-based reasoning using similarity and association knowledge | |
| Hruschka Jr et al. | Towards efficient variables ordering for Bayesian networks classifier | |
| Waad et al. | A three-stage feature selection using quadratic programming for credit scoring | |
| Verma et al. | Feature selection algorithm characterization for nids using machine and deep learning | |
| Al Nuaimi et al. | Online streaming feature selection with incremental feature grouping | |
| Siddiqi et al. | Detecting outliers in non-iid data: A systematic literature review | |
| Batal et al. | A bayesian scoring technique for mining predictive and non-spurious rules | |
| Sutera | Importance measures derived from random forests: characterisation and extension | |
| Alzubaidi et al. | LPCNN: convolutional neural network for link prediction based on network structured features | |
| Khan et al. | Mtl-nfw: A meta-learning framework for automated noise filter selection and hyperparameter optimization in auto-ml | |
| Sathiyamoorthi | Introduction to machine learning and its implementation techniques | |
| Chakraborty et al. | Feature Subset Selection Techniques with Machine Learning | |
| Arias et al. | A pairwise class interaction framework for multilabel classification | |
| Al-Dhaheri | A new feature selection method based on class association rule | |
| Li et al. | What will be popular next? predicting hotspots in two-mode social networks |