Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Automatica (Journal of IFAC)
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Obfuscation: Anonymity and Desensitization of Usable Data Sets
IEEE Security and Privacy
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
On elicitation of membership functions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An energy-gain bounding approach to robust fuzzy identification
Automatica (Journal of IFAC)
A new domain adaptation method based on rules discovered from cross-domain features
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
A multivariate classification of open source developers
Information Sciences: an International Journal
Hi-index | 22.14 |
In this study, we are concerned with system modeling which involves limited data and reconciles the developed model with some previously acquired domain knowledge being captured in the format of already constructed models. Each of these previously available models was formed on a basis of extensive data sets which are not available for the current identification pursuits. To emphasize the nature of modeling being guided by the reconciliation mechanisms, we refer to this mode of identification as experience-consistent modeling. The paper presents the conceptual and algorithmic framework by focusing on regression models. By forming a certain extended form of the performance index, it is shown that the domain knowledge captured by regression models can play a similar role as a regularization component used quite commonly in system identification. Experimental results involve both synthetic low-dimensional data and selected data coming from Machine Learning repository. The data used in the experiments tackle regression models as well as classification problems (two-class classifiers).