A One-Vs-One Classifier Ensemble With Majority Voting for Activity Recognition
A One-Vs-One Classifier Ensemble With Majority Voting for Activity Recognition
复制标题
具有多数投票的活动识别的一对一分类器集成
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
N. Bianchi
中科院分区：
文献类型：
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作者：
Bernardino Romera;Min S. H. Aung;N. Bianchi
提出了一种自动识别六种全身运动动作的解决方案。这个问题是由活动识别数据库[1]的发布提出的，并构成了2013欧洲人工神经网络研讨会分类比赛的基础。该数据集由30名受试者的运动特征组成，这些受试者使用单一设备传输加速度计和陀螺仪数据。在发布的数据集中包括在时间域和频域中的561个处理特征。提出的识别框架由线性支持向量机集成组成，每个支持向量机被训练成区分单个运动活动和另一个单个活动。多数投票规则被用来决定最终结果。为了进行比较，我们还实现了一个六个“赢家通吃”的多类支持向量机集成和k近邻模型。结果表明，在竞赛测试集上，一对一集成的系统准确率为96.4%。类似地，多类支持向量机集成和k近邻分类的准确率分别为93.7%和90.6%。一对一方法的结果被提交给比赛，从而产生了获胜的解决方案。
A solution for the automated recognition of six full body motion activities is proposed. This problem is posed by the release of the Activity Recognition database [1] and forms the basis for a classification competition at the European Symposium on Artificial Neural Networks 2013. The data-set consists of motion characteristics of thirty subjects captured using a single device delivering accelerometric and gyroscopic data. Included in the released data-set are 561 processed features in both the time and frequency domains. The proposed recognition framework consists of an ensemble of linear support vector machines each trained to discriminate a single motion activity against another single activity. A majority voting rule is used to determine the final outcome. For comparison, a six "winner take all" multiclass support vector machine ensemble and k-Nearest Neighbour models were also implemented. Results show that the system accuracy for the one versus one ensemble is 96.4% for the competition test set. Similarly, the multiclass SVM ensemble and k-Nearest Neighbour returned accuracies of 93.7% and 90.6% respectively. The outcomes of the one versus one method were submitted to the competition resulting in the winning solution.