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Pattern Recognition

05.14 : KUNCHEVA, L.I., HOARE, Z.S. & COCKCROFT, P.D.

Selection of independent binary features using probabilities: an example from veterinary medicine

Summary:

Published in:

Journal of Modern Applied Statistical Methods 4 (2005) 528-537.

05.15 : HADJITODOROV, S.T., KUNCHEVA, L.I. & TODOROVA, L.P.

Moderate diversity for better cluster ensembles

Summary:

Adjusted Rand index is used to measure diversity in cluster ensembles and a diversity measure is subsequently proposed. Although the measure was found to be related to the quality of the ensemble, this relationship appeared to be non-monotonic. In some cases, ensembles which exhibited a moderate level of diversity gave a more accurate clustering. Based on this, a procedure for building a cluster ensemble of a chosen type is proposed (assuming that an ensemble relies on one or more random parameters): generate a small random population of cluster ensembles, calculate the diversity of each ensemble and select the ensemble corresponding to the median diversity. We demonstrate the advantages of both our measure and procedure on 5 data sets and carry out statistical comparisons involving two diversity measures for cluster ensembles from the recent literature. An experiment with 9 data sets was also carried out to examine how the diversity-based selection procedure fares on ensembles of various sizes. For these experiments the classification accuracy was used as the performance criterion. The results suggest that selection by median diversity is no worse and in some cases is better than building and holding on to one ensemble.

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pdf file: shlkltif06.pdf

Published in:

Information Fusion 7 (2006) 264-275.

05.16 : MASIP, D., KUNCHEVA, L.I. & VITRIA, J.

An ensemble-based method for linear feature extraction for two-class problems

Summary:

Published in:

Pattern Analysis and Applications 8 (2005) 227-237.

05.17 : VILARINO, F., KUNCHEVA, L.I. & RADEVA, P.

ROC curves and video analysis optimization in intestinal capsule endoscopy

Summary:

Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions.

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pdf file: fvlkprprl06.pdf

Published in:

Pattern Recognition Letters (special issue on ROC analysis) 27 (2006) 875-881.

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