Automated detection of frontal systems from numerical model-generated data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Bit Reduction Support Vector Machine
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Automatic recognition of biological particles in microscopic images
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fast support vector machines for continuous data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Expert Systems with Applications: An International Journal
Feature subset selection for multi-class SVM based image classification
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Improving customer retention in financial services using kinship network information
Expert Systems with Applications: An International Journal
Kernel Factory: An ensemble of kernel machines
Expert Systems with Applications: An International Journal
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We present a system to recognize underwater plankton images from the shadow image particle profiling evaluation recorder (SIPPER). The challenge of the SIPPER image set is that many images do not have clear contours. To address that, shape features that do not heavily depend on contour information were developed. A soft margin support vector machine (SVM) was used as the classifier. We developed a way to assign probability after multiclass SVM classification. Our approach achieved approximately 90% accuracy on a collection of plankton images. On another larger image set containing manually unidentifiable particles, it also provided 75.6% overall accuracy. The proposed approach was statistically significantly more accurate on the two data sets than a C4.5 decision tree and a cascade correlation neural network. The single SVM significantly outperformed ensembles of decision trees created by bagging and random forests on the smaller data set and was slightly better on the other data set. The 15-feature subset produced by our feature selection approach provided slightly better accuracy than using all 29 features. Our probability model gave us a reasonable rejection curve on the larger data set.