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Multi-label classification in social network environments is becoming a key area of data mining research in recent years. Given some nodes' labels (i.e., the sources), the task is to infer some other nodes' labels (i.e., the targets) in the same network. Relational classification methods, which leverage the correlation of labels between linked instances, have been shown to outperform traditional classifiers...
To overcome the defect of slow convergence speed, precocity and stagnation in the classical ACO algorithm,the authors propose an Improved Ant System Algorithm Based on PPL to solve TSP according to pheromone updating features of Ant System algorithm, combined with PPL (Parallel Pattern Library) parallel programming idea. The new algorithm combines three different pheromone update methods to make a...
In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available before learning begins. Feature generation and selection often have to be interleaved. Managing streaming features has been extensively studied in classification, but little attention has been paid to the problem of causal...
In this paper, a new scalability of hybrid fuzzy clustering algorithm that incorporates the Fuzzy C-means into the Quantum-behaved Particle Swarm Optimization algorithm is proposed. The QPSO has less parameters and higher convergent capability of the global optimizing than Particle Swarm Optimization algorithm. So the iteration algorithm is replaced by the new hybrid algorithm based on the gradient...
The standard causal discovery assumes that all variables are available from the beginning. In this paper, we consider an untouched scenario in which not all variables are available in advance. We call this scenario online causal discovery which assumes that the target of interest is given in advance while the other variables are unknown. With this situation, an online algorithm is presented which...
Fuzzy Kohonen clustering networks (FKCN) are well known for clustering analysis (unsupervised learning and self-organizing). This classification of FKCN algorithm is a set of iterative procedures that suffer some major problems, for example its constringency rate is not too fast for a large amount of datasets. To overcome these defects, an efficient fuzzy Kohonen network algorithm is proposed in this...
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