In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

在许多机器学习场景中，寻找适合特定数据集的最佳分类器可能会花费大量时间和资源。此外，它可能需要对特定领域的深入了解。我们提出了一种新技术，该技术不需要该领域的专业知识，并且避免了通常使用的超参数调整和模型选择策略。我们的方法是一种创新的集成技术，它对一组随机生成的分类器使用投票规则。给定一个新的输入样本，我们将每个分类器的输出解释为对所有可能分类的排名。然后，我们使用投票规则汇总这些输出排名，该规则将它们视为对类的偏好。我们证明，与最新技术相比，我们的方法取得了良好的效果，