One of the most popular families of techniques to boost classification are Ensemble methods. Random Forests, Bagging and Boosting are the most popular and widely used ones. This article presents a novel Ensemble Model, named Random Granular Reflections. The algorithm used in this new approach creates an ensemble of homogeneous granular decision systems. The first step of the learning process is to take the training system and cover it with random homogeneous granules (groups of objects from the same decision class that are as little indiscernible from each other as possible). Next, granular reflection is created, which is finally used in the classification process. Results obtained by our initial experiments show that this approach is promising and comparable with other tested methods. The main advantage of our new method is that it is not necessary to search for optimal parameters while looking for granular reflections in the subsequent iterations of our ensemble model.

中文翻译：

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新型合奏模型-随机颗粒反射

增强分类的最流行的技术之一是合奏方法。随机森林，装袋和助推是最受欢迎和广泛使用的森林。本文介绍了一个名为“随机颗粒反射”的新颖合奏模型。这种新方法中使用的算法创建了齐次的粒状决策系统集合。学习过程的第一步是采用训练系统，并用随机的均质颗粒（来自同一决策类的对象组，彼此之间尽可能少地区分）覆盖它。接下来，创建粒状反射，最终将其用于分类过程。通过我们的初始实验获得的结果表明，这种方法很有希望，并且可以与其他经过测试的方法相媲美。