论文标题
线性分类器组合通过多个潜在功能
Linear Classifier Combination via Multiple Potential Functions
论文作者
论文摘要
基于分类的模型构建过程的重要方面是评分函数的校准。校准过程的弱点之一是，它没有考虑到有关特征空间中公认对象的相对位置的信息。为了减轻这一局限性，在本文中，我们提出了一个新颖的概念，该概念是根据对象与决策边界及其与类质体的距离的距离计算得分函数的。一个重要的属性是，所提出的分数函数对于所有线性碱基分类器具有相同的性质，这意味着这些分类器的输出同样表示并且具有相同的含义。将所提出的方法与其他集合算法进行了比较，并且在多个龙骨数据集上的实验证明了我们方法的有效性。为了讨论我们的实验结果，我们使用多个分类性能度量和统计分析。
A vital aspect of the classification based model construction process is the calibration of the scoring function. One of the weaknesses of the calibration process is that it does not take into account the information about the relative positions of the recognized objects in the feature space. To alleviate this limitation, in this paper, we propose a novel concept of calculating a scoring function based on the distance of the object from the decision boundary and its distance to the class centroid. An important property is that the proposed score function has the same nature for all linear base classifiers, which means that outputs of these classifiers are equally represented and have the same meaning. The proposed approach is compared with other ensemble algorithms and experiments on multiple Keel datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures and statistical analysis.
