Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Principles of data mining
Advanced Engineering Mathematics with MATLAB
Advanced Engineering Mathematics with MATLAB
An introduction to variable and feature selection
The Journal of Machine Learning Research
An overview of regression techniques for knowledge discovery
The Knowledge Engineering Review
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Features Selection Using Fuzzy ESVDF for Data Dimensionality Reduction
ICCET '09 Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 01
A generic framework for real-time discrete event simulation (DES) modelling
Proceedings of the 40th Conference on Winter Simulation
Advanced Engineering Informatics
A virtual metrology system for semiconductor manufacturing
Expert Systems with Applications: An International Journal
Dual features functional support vector machines for fault detection of rechargeable batteries
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Combination of feature selection approaches with SVM in credit scoring
Expert Systems with Applications: An International Journal
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Explorations in Monte Carlo Methods
Explorations in Monte Carlo Methods
Content-Based Classification and Retrieval of Wild Animal Sounds Using Feature Selection Algorithm
ICMLC '10 Proceedings of the 2010 Second International Conference on Machine Learning and Computing
Classification of Hyperspectral Image with Feature Selection and Parameter Estimation
ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 01
Nonlinear distortion analysis via perturbation method
International Journal of Circuit Theory and Applications
Study of feature selection for the stored-grain insects based on artificial immune algorithm
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Improving vector space embedding of graphs through feature selection algorithms
Pattern Recognition
Intelligent machine agent architecture for adaptive control optimization of manufacturing processes
Advanced Engineering Informatics
An evaluation of filter and wrapper methods for feature selection in categorical clustering
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Feature selection for dimensionality reduction
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Advanced Engineering Informatics
Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)
IEEE Transactions on Knowledge and Data Engineering
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The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling. Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system. The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.