2006 Special issue: Machine learning in soil classification

Authors:
B. Bhattacharya;D. P. Solomatine
Affiliations:
Hydroinformatics and Knowledge Management Department, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands;Hydroinformatics and Knowledge Management Department, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands
Venue:
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Year:
2006

Citing 5
Cited 2

A survey of constrained classification

Computational Statistics & Data Analysis
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
An algorithm for clustering and classification of series data with constraint of contiguity

Design and application of hybrid intelligent systems

Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality

Journal of Biomedical Informatics
Recent advances and future challenges for artificial neural systems in geotechnical engineering applications

Advances in Artificial Neural Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc. intervals of measured series data (signals) are to be attributed a class maintaining the constraint of contiguity 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 developed and 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 to assign classes to the segments classifiers are built; they employ Decision Trees, ANN and Support Vector Machines. The methodology was tested in classifying sub-surface soil using measured data from Cone Penetration Testing and satisfactory results were obtained.