In online advertising, a change in the publisher's actual status label with every generated click shows the suspicious behaviour of the publisher. Furthermore, only a small proportion of the clicks generated by th...
详细信息
In online advertising, a change in the publisher's actual status label with every generated click shows the suspicious behaviour of the publisher. Furthermore, only a small proportion of the clicks generated by the publishers are invalid, resulting in class skewness in the dataset and a challenging issue for the conventional classification methods as they get biased towards the outnumbered class. This suspicious behaviour of publishers with an uneven class distribution ratio adversely affects the classifier's performance and increases model complexities. Thus, developing machine-learning methods capable of producing efficacious predictive models towards detecting fraudulent publishers is pivotal. This paper's novel stacked generalization framework comprises two stacked generalization architectures, one for resampling and the second for classification. The framework employs a stacked generalization approach using generalizers to improve the learning model's performance in two steps: first, reducing the error rate of algorithms towards reducing the bias in a learning set. Second, the results obtained through level-0 generalizers are fed as input to the level-1 generalizer with stacked integrated output towards combining the predictions for improving the predictive performance. Broad experimentations are conducted on FDMA 2012 user click dataset using ten-fold cross-validation. The performance of the proposed architecture is generalized by performing experiments on eight other highly imbalanced benchmark datasets, and performance is measured using average precision, recall, and F1-score. Results empirically prove the superiority of the proposed architecture in the publisher's behaviour prediction and classification as legitimate or illegitimate.
Wireless sensor networks (WSNs) do not have a fixed infrastructure and consist of sensor nodes that perform sensing and communicating tasks. The WSNs have large application spectrum such as habitat monitoring, militar...
详细信息
Wireless sensor networks (WSNs) do not have a fixed infrastructure and consist of sensor nodes that perform sensing and communicating tasks. The WSNs have large application spectrum such as habitat monitoring, military surveillance, and target tracking, where sensor nodes may operate distributed in highly dynamic environments. Battery-constrained sensor nodes may aggregate the sensed data, localize themselves, and route the packets in an energy-efficient and decentralized manner to enable running the applications. Agents are capable of independent and autonomous action, so that they can successfully carry out tasks that have been delegated to them, thus agent-based approaches are very suitable to apply as the solution of the problems occurring in WSNs. So far many agent-based approaches were proposed for WSNs. This paper surveys the agent technologies for sensor networks by providing a classification, objectives and costs of these approaches with the open research problems. To the best of our knowledge, this is the first study that covers the intersection of the agent technology and sensor networks from a wide perspective.
暂无评论