The Neyman-Pearson (NP) paradigm in binary classification treats type I and type II errors with different priorities. It seeks classifiers that minimize type II error, subject to a type I error constraint under a user specified level α. In this paper, plug-in classifiers are developed under the NP paradigm. Based on the fundamental Neyman-Pearson Lemma, we propose two related plug-in classifier...

Receiver-operating characteristic (ROC) analysis has proven to be a powerful method for dealing with misclassification costs and skewed class distributions (Provost & Fawcett, 1998). In the typical representation, an ROC analysis evaluates false accept versus false reject rates for a set of candidate binary classifiers under all possible (linear) cost and prior class distribution assumptions. T...

The One-vs-One strategy is one of the most commonly used decomposition technique to overcome multi-class classification problems; this way, multi-class problems are divided into easier-to-solve binary classification problems considering pairs of classes from the original problem, which are then learned by independent base classifiers. The way of performing the division produces the so-called no...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy OPTIMIZED DICTIONARY DESIGN AND CLASSIFICATION USING THE MATCHING PURSUITS DISSIMILARITY MEASURE By Raazia Mazhar May 2009 Chair: Paul D. Gader Co-chair: Joseph N. Wilson Major: Computer Engineering Discrimination-based classifiers diffe...

Classifier performance evaluation is an important step in designing diagnostic systems. The purposes of performing classifier performance evaluation include: 1) to select the best classifiers from the several candidate classifiers, 2) to verify that the classifier designed meets the design requirement, and 3) to identify the need for improvements in the classifier components. In order to effect...

This paper proposes a method to perform class-specific feature selection in multiclass support vector machines addressed with the one-against-all strategy. The main issue arises at the final step of the classification process, where binary classifier outputs must be compared one against another to elect the winning class. This comparison may be biased towards one specific class when the binary ...

A method based on one class support vector machine (OCSVM) is proposed for class incremental learning. Several OCSVM models divide the input space into several parts. Then, the 1VS1 classifiers are constructed for the confuse part by using the support vectors. During the class incremental learning process, the OCSVM of the new class is trained at first. Then the support vectors of the old class...

Classifier fusion techniques are gaining more popularity for their capability of improving the accuracy achieved by individual classifiers. A common approach is to combine the classifiers’ outcome using simple methods, such as majority voting. In this paper, we build a meta-classifier by fusing some already well-known classifiers for protein structure prediction. Each individual classifier outp...

Supervised classifiers are commonly employed in remote sensing to extract land cover information, but various factors affect their accuracy. The number of available training samples, in particular, is known to have a significant impact on classification accuracies. Obtaining a sufficient number of samples is, however, not always practical. The support vector machine (SVM) is a supervised classi...