Combination of linear classifiers using score function -- analysis of possible combination strategies

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the proposed approach was applied to the heterogeneous ensemble and it was compared to two reference methods -- majority voting and model averaging respectively. The comparison was made in terms of seven different quality criteria. The result shows that combination strategies based on simple average, and trimmed average are the best combination strategies of the geometrical combination

Bagging for linear classifiers

Classifiers built on small training sets are usually biased or unstable. Different techniques exist to construct more stable classifiers. It is not clear which ones are good, and whether they really stabilize the classifier or just improve the performance. In this paper bagging (bootstrapping and aggregating (1) ) is studied for a number of linear classifiers. A measure for the instability of classifiers is introduced. The influence of regularization and bagging on this instability and the generalization error of linear classifiers is investigated. In a simulation study it is shown that in general bagging is not a stabilizing technique. It is also demonstrated that one can consider the instability of the classifier to predict how useful bagging will be. Finally, it is shown experimentally that bagging might improve the performance of the classifier only for very unstable situations

Effectiveness of Spectral Band Selection/Extraction Techniques for Spectral Data

Abstract. In the past few years a variety of successful algorithms to select/ extract discriminative spectral bands was introduced. By exploiting the connectivity of neighbouring spectral bins, these techniques may be more beneficial than the standard feature selection/extraction methods applied for spectral classification. The goal of this paper is to study the effect of the training sample size on the performance of different strategies to select/extract informative spectral regions. We also consider the success of these methods compared to Principal Component Analysis (PCA) for different numbers of extracted components/groups of spectral bands.