基于LFOA-HSRVM的IPTV用户报障预测方法

Prediction method for IPTV user’s complaint based on LFOA-HSRVM algorithm

针对交互式网络电视(IPTV)用户报障因素复杂、故障样本相对贫瘠的问题,基于相关向量机(RVM)高稀疏性的建模特点提出一种结合RVM参数优化和混合采样的IPTV用户报障预测方法(LFOA-HSRVM)。该方法将IPTV的用户报障预测视为一个针对非均衡数据集的二分类问题,克服了传统RVM算法在处理非均衡数据时决策边界偏向少数类样本的问题。实验表明,与其他相关算法相比,该算法的少数类分类性能和总体分类性能均有较大提升,能获得更好的报障预测效果。

Aiming at the problems of numerous complaint factors and relatively poor fault samples of Internet protocol TV users,this paper proposed the LFOA-HSRVM algorithm as a prediction method for IPTV user’s complaint combined with the outstanding sparsity of relevance vector machine.The method regarded fault prediction of IPTV user as a binary-classification problem with imbalanced data sets.The algorithm was based on the nuclear parameter optimization of RVM and hybrid sampling technology to overcome the problem that the decision boundary of the traditional RVM algorithm was biased to the minority class when dealing with imbalanced data sets.Compared with other algorithms,the experimental results show that the algorithm’s performance of few classification and overall classification are improved greatly,and the effect of prediction is better.