Flat Classification Research Articles

Classification of sediments on exposed tidal flats in the German Bight using multi-frequency radar data

We present a new method for the extraction of roughness parameters of sand ripples on exposed tidal flats from multi-frequency synthetic aperture radar (SAR) data. The method is based on the Integral Equation Model (IEM) which predicts the normalized radar cross-section (NRCS) of randomly rough dielectric surfaces. The data used for this analysis were acquired in the German Bight of the North Sea by the Spaceborne Imaging Radar-C/X-Band SAR (SIR-C/X-SAR) in 1994. In-situ measurements of the root-mean-squared (rms) height and the correlation length of the sand ripples clearly demonstrate a relationship between these roughness parameters and the C-band NRCS determined from an ERS SAR image. Using the IEM we have calculated NRCS isolines for the three frequency bands deployed by SIR-C/X-SAR (L, C, and X band), as a function of the rms height and the correlation length of the sand ripples. For each SIR-C/X-SAR image pixel these two roughness parameters were determined from the intersections of the NRCS isolines at different radar bands, and they were used for a crude sediment classification for a small test area at the German North Sea coast. Comparing our results with available sediment maps, we conclude that the presented method is very promising for tidal flat classification by using data from presently existing airborne and future spaceborne multi-frequency SAR systems.

Classifying web documents in a hierarchy of categories: a comprehensive study

Most of the research on text categorization has focused on classifying text documents into a set of categories with no structural relationships among them (flat classification). However, in many information repositories documents are organized in a hierarchy of categories to support a thematic search by browsing topics of interests. The consideration of the hierarchical relationship among categories opens several additional issues in the development of methods for automated document classification. Questions concern the representation of documents, the learning process, the classification process and the evaluation criteria of experimental results. They are systematically investigated in this paper, whose main contribution is a general hierarchical text categorization framework where the hierarchy of categories is involved in all phases of automated document classification, namely feature selection, learning and classification of a new document. An automated threshold determination method for classification scores is embedded in the proposed framework. It can be applied to any classifier that returns a degree of membership of a document to a category. In this work three learning methods are considered for the construction of document classifiers, namely centroid-based, naïve Bayes and SVM. The proposed framework has been implemented in the system WebClassIII and has been tested on three datasets (Yahoo, DMOZ, RCV1) which present a variety of situations in terms of hierarchical structure. Experimental results are reported and several conclusions are drawn on the comparison of the flat vs. the hierarchical approach as well as on the comparison of different hierarchical classifiers. The paper concludes with a review of related work and a discussion of previous findings vs. our findings.

Hierarchical Text Categorization Using Neural Networks

This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.