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首页> 外文期刊>International journal of web services research >A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users
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A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users

机译:一种新的基于多层分类集成的社会用户位置预测方法

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摘要

Information-disclosure by social-users has increased enormously. Using this information for accurate location-prediction is challenging. Thus, a novel Multi-Layer Ensemble Classification scheme is proposed. It works on un-weighted/weighted majority voting, using novel weight-assignment function. Base learners are selected based on their individual performances for training the model. Main motive is to develop an efficient approach for check-ins-based location-classification of social-users. The proposed model is implemented on Foursquare datasets where a classification accuracy of 94% is achieved, which is higher than other state-of-the-art techniques. Apart from tracking locations of social-users, proposed framework can be useful for detecting malicious users present in various expert and intelligent-system.
机译:社会用户的信息披露已大大增加。使用此信息进行准确的位置预测具有挑战性。因此,提出了一种新颖的多层集合分类方案。它使用新颖的权重分配功能处理未加权/加权多数投票。根据基础学习者的个人表现来选择他们,以训练模型。主要动机是开发一种用于基于签入的社交用户位置分类的有效方法。所提出的模型在Foursquare数据集上实现,该数据集的分类精度达到94%,高于其他最新技术。除了跟踪社交用户的位置以外,提出的框架还可用于检测各种专家和智能系统中存在的恶意用户。

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