A Novel Ensemble Model - The Random Granular Reflections

Abstract

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.

Rough Sets Turn 40: From Information Systems to Intelligent Systems

“…When thinking about the future it is also worth referring to the history. That reflected one more objective: acknowledging the 40th anniversary of rough sets [1], their founder [5], as well as some of relevant past and present events such as FedCSIS 2012 (rough set papers published exactly 10 years ago) [47], [48], [51], [53], [54], [57], [58], [59], [61], [64], [66], the PP-RAI 2022 rough set contest [69], [70], [72], [73], [77], [78], [81], [82], [83], [84], [89], [90], [91], [95], [96], [97], [98], [101], [105], [106] and celebration of the 30th CS&P -the event series whereby this paper's topics have been regularly addressed [36], [40], [44], [103], [116], [123].…”

“…Finally, papers [105], [106] combine the principles of rough sets and GrC with popular machine learning methods, referring to decision model ensembles as well. The idea is to prepare compacted data inputs -called granular reflections [15] -for the algorithms responsible for learning decision models such as e.g.…”

Application of Random Sampling in the Concept-Dependent Granulation Method

“…The effectiveness of granulation methods (according to Polkowski's scheme) has been verified in many contexts and works with basically every popular classifier from SVM [8], decision trees [10] to neural networks [9]. The methods have also found applications in the context of steganography [11], preprocessing before feeding data into neural networks [9], in ensemble models [7], in classification processes [12], for absorbing missing values [13], in localization of mobile robots under magnetically variable conditions [14]. Due to the computational complexity of our techniques, an area for exploration that has not yet been adequately explored is their use for with methods for dealing with Big Data.…”