Data Mining Methods and Models
Data Mining Methods and Models
Support Vector Machines for Visualization and Dimensionality Reduction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
A Soft Computing System to Perform Face Milling Operations
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
A PCA-based technique to detect moving objects
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding
International Journal of Computer Vision
Journal of Intelligent Manufacturing
A non-linear quality improvement model using SVR for manufacturing TFT-LCDs
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
Towards the reduction of data used for the classification of network flows
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
The evolutionary development of roughness prediction models
Applied Soft Computing
Improvement of surface roughness models for face milling operations through dimensionality reduction
Integrated Computer-Aided Engineering
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Soft computing techniques are frequently used to develop data-driven prediction models. When modelling of an industrial process is planned, experiments in a real production environment are frequently required to collect the data. As a consequence, in many cases the experimental data sets contain only limited number of valuable records acquired in expensive experiments. This is accompanied by a relatively high number of measured variables. Hence, the need for dimensionality reduction of many industrial data sets. The primary objective of this study is to experimentally assess one of the most popular approaches based on the use of principal component analysis and multilayer perceptrons. The way the reduced dimension could be determined is investigated. A method aiming to control the dimensionality reduction process in view of model prediction error is evaluated. The proposed method is tested on two industrial data sets. The prediction improvement arising from the proposed technique is discussed.