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In an earlier paper, we proposed a new negative correlation learning (NCL) algorithm for classification ensembles, called AdaBoost.NC, which has significantly better performance than the standard AdaBoost and other NCL algorithms on many benchmark data sets with low computation cost. In this paper, we give deeper insight into this algorithm from both theoretical and experimental aspects to understand...
The widespread use of positioning technologies ranging from GSM and GPS to WiFi devices, tend to produce large-scale datasets of trajectories, representing the movement of travelling entities. Several applications may benefit from mining such datasets. However, mining results only become truly useful and meaningful for the end user when the intrinsically complex nature of the movement data in terms...
In the design of Classifier Ensembles, diversity is considered as one of the main aspects to be taken into account, since there is no gain in combining identical classification methods. One way of increasing diversity is to use feature selection methods in order to select subsets of attributes for the individual classifiers. In this paper, it is investigated the use of a simple reinforcement-based...
In the context of ensemble systems, feature selection methods can be used to provide different subsets of attributes for the individual classifiers, aiming to reduce redundancy among the attributes of a pattern and to increase the diversity in such systems. Among the several techniques that have been proposed in the literature, optimization methods have been used to find the optimal subset of attributes...
This paper proposes a new negative correlation learning (NCL) algorithm, called AdaBoost.NC, which uses an ambiguity term derived theoretically for classification ensembles to introduce diversity explicitly. All existing NCL algorithms, such as CELS and NCCD, and their theoretical backgrounds were studied in the regression context. We focus on classification problems in this paper. First, we study...
Computational complexity is one of the most important issues in any machine-learning algorithm. A novel working set selection mechanism is proposed to improve Support Vector Machine (SVM) learning. Implementation is based on the Keerthi et al.'s SMO algorithm, but our approach is one-class classification. When selecting samples for the optimization process, much effort is spent to find the most violating...
Classifier ensembles are systems composed of a set of individual classifiers (organized in a parallel way) and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account, since there is no gain in combining identical classification methods. One way of increasing...
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