Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Instance-Based Learning Algorithms
Machine Learning
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Neural networks and the bias/variance dilemma
Neural Computation
Growing a tree classifier with imprecise data
Pattern Recognition Letters
Machine Learning
Machine Learning
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
An introduction to symbolic data analysis and the SODAS software
Intelligent Data Analysis
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Renyi's divergence and entropy rates for finite alphabet Markov sources
IEEE Transactions on Information Theory
A Symbolic Pattern Classifier for Interval Data Based on Binary Probit Analysis
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Optimization algorithm for learning consistent belief rule-base from examples
Journal of Global Optimization
A modal symbolic classifier for interval data
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A weighted learning vector quantization approach for interval data
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Symbolic data analysis aims at generalizing some standard statistical data mining methods, such as those developed for classification tasks, to the case of symbolic objects (SOs). These objects synthesize information concerning a group of individuals of a population, eventually stored in a relational database, and ensure confidentiality of original data. Classifying SOs is an important task in symbolic data analysis. In this paper a lazy-learning approach that extends a traditional distance weighted k-Nearest Neighbor classification algorithm to SOs, is presented. The proposed method has been implemented in the system SO-NN (Symbolic Objects Nearest Neighbor) and evaluated on symbolic datasets.