Cost-sensitive learning relies on the availability of a known and fixed cost matrix. However, in some scenarios, the cost matrix is uncertain during training, and re-train a classifier after the cost matrix is specified would not be an option. For binary classification, this issue can be successfully addressed by methods maximizing the Area Under the ROC Curve (AUC) metric. Since the AUC can me...

The purpose and goal of this assignment was to design and implement a small system to predict a class of a single numerical variable in a binomial class problem. In order to do this, it was requested to implement or to think of one or more “1D classifiers”, discussing their computational complexities in the learning phases, the performances on the given dataset, the differences and similarities...

The goal of pattern classification can be approached from two points of view: informative where the classifier learns the class densities, or discriminative where the focus is on learning the class boundaries without regard to the underlying class densities. We review and synthesize the tradeoffs between these two approaches for simple classifiers, and extend the results to modern techniques su...

We present specializing, a method for combining classifiers for multi-class classification. Specializing trains one specialist classifier per class and utilizes each specialist to distinguish that class from all others in a one-versus-all manner. It then supplements the specialist classifiers with a catch-all classifier that performs multi-class classification across all classes. We refer to th...

In this paper we report the contribution of XRCE team to the Domain Adaptation Challenge [10] organized in the framework of ImageCLEF 2014 competition [9]. We describe our approach to build an image classification system when a weak image annotation in the target domain is compensated by massively annotated images in source domains. One method is based using several heterogeneous methods for th...

Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) trained reconstruct normal only training data with expectation that, test time, it can poorly data. However, previous studies have shown even data, AEs often well, resulting a decreased performance. To mitigate this problem, we p...

This paper proposes a new ensemble method that constructs an ensemble of tree-structured classifiers using multi-view learning. We are motivated by the fact that an ensemble can outperform its members providing that these classifiers are diverse and accurate. In order to construct diverse individual classifiers, we assume that the object to be classified is described by multiple feature sets (v...

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Senti...