A survey of constrained classification
Computational Statistics & Data Analysis
Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Intelligent clustering with instance-level constraints
Intelligent clustering with instance-level constraints
2006 Special issue: Machine learning in soil classification
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
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Clustering and classification of series-based data is an important issue in a number of engineering problems, in particular in geotechnics. In such problems intervals of measured seria (signals) are. to be attributed a class so that the constraint of contiguity have to be considered and standard classification methods could be inadequate. Classification in this case needs involvement of an expert who observes the magnitude and trends of the signals in addition to any a priori information that might be available. In this paper an approach for automating this classification procedure is presented. Firstly, a segmentation algorithm is applied to segment the measured signals. Secondly, the salient features of these segments are extracted using boundary energy method. Based on the measured data and extracted features classifiers to assign class values to the segments were built; they employ decision trees and artificial neural networks. The algorithm was tested in a case-study for classifying sub-surface soil using measured data from cone penetration testing and satisfactory results were obtained.