Advances in neural information processing systems 2
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Pairwise classification and support vector machines
Advances in kernel methods
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Variable selection using svm based criteria
The Journal of Machine Learning Research
Rough Set-Aided Feature Selection for Automatic Web-Page Classification
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Identifying mobile phone design features critical to user satisfaction
Human Factors in Ergonomics & Manufacturing
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
Design of input vector for day-ahead price forecasting of electricity markets
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Expert Systems with Applications: An International Journal
Fractal features for localization of temporal lobe epileptic foci using SPECT imaging
Computers in Biology and Medicine
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Computers and Industrial Engineering
Genetic fuzzy modeling of user perception of three-dimensional shapes
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Expert Systems with Applications: An International Journal
Product form feature selection for mobile phone design using LS-SVR and ARD
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: context diversity - Volume Part III
A multi-objective genetic algorithm approach to rule mining for affective product design
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
Hi-index | 12.06 |
Various form features affect consumer preference regarding product design. It is, therefore, important that designers identify these critical form features to aid them in developing appealing products. However, the problems inherent in choosing product form features have not yet been intensively investigated. In this paper, an approach based on multiclass support vector machine recursive feature elimination (SVM-RFE) is proposed to streamline the selection of optimum product form features. First, a one-versus-one (OVO) multiclass fuzzy support vector machines (multiclass fuzzy SVM) model using a Gaussian kernel was constructed based on product samples from mobile phones. Second, an optimal training model parameter set was determined using two-step cross-validation. Finally, a multiclass SVM-RFE process was applied to select critical form features by either using overall ranking or class-specific ranking. The weight distribution of each iterative step can be used to analyze the relative importance of each of the form features. The results of our experiment show that the multiclass SVM-RFE process is not only very useful for identifying critical form features with minimum generalization errors but also can be used to select the smallest feature subset for building a prediction model with a given discrimination capability.