Shehab et al., 2022 - Google Patents
Toward feature selection in big data preprocessing based on hybrid cloud-based modelShehab et al., 2022
- Document ID
- 637541096519348129
- Author
- Shehab N
- Badawy M
- Ali H
- Publication year
- Publication venue
- The Journal of Supercomputing
External Links
Snippet
Recently, big data are widely noticed in many fields like machine learning, pattern recognition, medical, financial, and transportation fields. Data analysis is crucial to converting data into more specific information fed to the decision-making systems. With the …
- 238000007781 pre-processing 0 title abstract description 33
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
- 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
- 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
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
-
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
-
- 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
- G06K9/6267—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
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Neelakandan et al. | RETRACTED ARTICLE: An automated exploring and learning model for data prediction using balanced CA-SVM | |
Zhou et al. | Machine learning on big data: Opportunities and challenges | |
Ramírez-Gallego et al. | An information theory-based feature selection framework for big data under apache spark | |
Dubey et al. | An efficient ACO-PSO-based framework for data classification and preprocessing in big data | |
Shehab et al. | Toward feature selection in big data preprocessing based on hybrid cloud-based model | |
Meera et al. | Retracted article: a hybrid metaheuristic approach for efficient feature selection methods in big data | |
Arnaiz-González et al. | MR-DIS: democratic instance selection for big data by MapReduce | |
HajKacem et al. | Overview of scalable partitional methods for big data clustering | |
Devi et al. | Swarm intelligent based online feature selection (OFS) and weighted entropy frequent pattern mining (WEFPM) algorithm for big data analysis | |
Pięta et al. | Applications of rough sets in big data analysis: an overview | |
Shobanadevi et al. | Data mining techniques for IoT and big data—A survey | |
Srivani et al. | Literature review and analysis on big data stream classification techniques | |
Sreedhara et al. | Efficient big data clustering using adhoc fuzzy C means and auto-encoder CNN | |
Cao et al. | A parallel Adaboost-backpropagation neural network for massive image dataset classification | |
Sarumathiy et al. | RETRACTED ARTICLE: Improvement in Hadoop performance using integrated feature extraction and machine learning algorithms | |
Zhang et al. | MapReduce-based distributed tensor clustering algorithm | |
Safhi et al. | Data intelligence in the context of big data: A survey | |
Wu et al. | Cost-sensitive decision tree with multiple resource constraints | |
Desai et al. | Distributed decision tree | |
Bousbaci et al. | Efficient data distribution and results merging for parallel data clustering in mapreduce environment | |
Pokorný | Big data storage and management: Challenges and opportunities | |
Potla | Scalable Machine Learning Algorithms for Big Data Analytics: Challenges and Opportunities | |
Danesh et al. | Ensemble-based clustering of large probabilistic graphs using neighborhood and distance metric learning | |
Lin et al. | Synthesizing decision rules from multiple information sources: a neighborhood granulation viewpoint | |
Stanovic et al. | Maximal independent vertex set applied to graph pooling |