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Kernel combination is meant to improve the performance of single kernels and avoid the difficulty of kernel selection. The most common way of combining kernels is to compute their weighted sum. Usually, the kernels are assumed to exist in independent empirical feature spaces and therefore were combined without considering their relationships. To take these relationships into consideration in...
It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a...
Multiple Kernel Learning (MKL) has become a preferred choice for information fusion in image recognition problem. Aim of MKL is to learn optimal combination of kernels formed from different features, thus, to learn importance of different feature spaces for classification. Augmented Kernel Matrix (AKM) has recently been proposed to accommodate for the fact that a single training example may have different...
In this paper we formulate multiple kernel learning (MKL) as a distance metric learning (DML) problem. More specifically, we learn a linear combination of a set of base kernels by optimising two objective functions that are commonly used in distance metric learning. We first propose a global version of such an MKL via DML scheme, then a localised version. We argue that the localised version not only...
The goal of one-class classification is to distinguish the target class from all the other classes using only training data from the target class. Because it is difficult for a single one-class classifier to capture all the characteristics of the target class, combining several one-class classifiers may be required. Previous research has shown that the Random Subspace Method (RSM), in which classifiers...
In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original...
In the current paper we consider the task of object classification in wireless sensor networks. Due to restricted battery capacity, minimizing the energy consumption is a main concern in wireless sensor networks. Assuming that each feature needed for classification is acquired by a sensor, a sequential classifier combination approach is proposed that aims at minimizing the number of features used...
One assumption in supervised learning is that data is independent and identically distributed. However, this assumption does not hold true in many real cases. Sequential learning is that discipline of machine learning that deals with dependent data. In this paper, we revise the Multi-Scale Sequential Learning approach (MSSL) for applying it in the multi-class case (MMSSL). We have introduced...
This paper proposes a method for constructing ensembles of decision trees: GRASP Forest. This method uses the metaheuristic GRASP, usually used in optimization problems, to increase the diversity of the ensemble. While Random Forest increases the diversity by randomly choosing a subset of attributes in each tree node, GRASP Forest takes into account all the attributes, the source of randomness in...
Ensembles of decision trees are considered for imbalanced datasets. Conventional decision trees (C4.5) and trees for imbalanced data (CCPDT: Class Confidence Proportion Decision Tree) are used as base classifiers. Ensemble methods, based on undersampling and oversampling, for imbalanced data are considered. Conventional ensemble methods, not specific for imbalanced data, are also studied: Bagging,...
This paper introduces a novel splitting criterion parametrized by a scalar ‘α’ to build a class-imbalance resistant ensemble of decision trees. The proposed splitting criterion generalizes information gain in C4.5, and its extended form encompasses Gini(CART) and DKM splitting criteria as well. Each decision tree in the ensemble is based on a different splitting criterion enforced by a distinct α...
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