Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function. Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.

Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches.

The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology.

Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of the improved classification accuracy in different applications. A large variety of ensemble methods have been proposed in order to exploit strengths of individual classifiers. In this paper, we present a unifying framework for multiple classifier systems, which unites most classification methods by an ensemble of classifiers. Specifically, we link two research lines in machine learning: multiclass classification based on the class binarization techniques and the strategies of ensemble classification. With the proposed framework, the various ensemble classification strategies will be broadly categorized into four main approaches. Then, we provide a brief survey of ensemble methods based on these main approaches as well as principle techniques proposed to combine them.

Among the proposed methods to deal with multi-class classification problems, the Error-Correcting Output Codes (ECOC) represents a powerful framework. The key factor in designing any ECOC matrix is the independency of the binary classifiers, without which the ECOC method would be ineffective. This paper proposes an efficient new approach to the ECOC framework in order to improve independency among classifiers. The underlying rationale for our work is that we design three-dimensional codematrix, where the third dimension is the feature space of the problem domain. Using rough set-based feature selection, a new algorithm, named "Rough Set Subspace ECOC (RSS-ECOC)" is proposed. We introduce the Quick Multiple Reduct algorithm in order to generate a set of reducts for a binary problem, where each reduct is used to train a dichotomizer. In addition to creating more independent classifiers, ECOC matrices with longer codes can be built. The numerical experiments in this study compare the classification accuracy of the proposed RSS-ECOC with classical ECOC, one-versus-one, and one-versus-all methods on 24 UCI datasets. The results show that the proposed technique increases the classification accuracy in comparison with the state of the art coding methods.