A comparative evaluation of aggregation methods for machine learning over vertically partitioned data. (15th August 2020)

• Record Type: Journal Article
• Authors: Trevizan, Bernardo; Chamby-Diaz, Jorge; Bazzan, Ana L.C.; Recamonde-Mendoza, Mariana
• Is Part Of: Expert systems with applications. Volume 152(2020)
• Publication Date: 2020-08-15
• Subjects: Vertical data partitioning -- Distributed machine learning -- Classification -- Predictions aggregation -- Attribute-partitioned data; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113406
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We compare aggregation methods for vertically partitioned data in several scenarios. Impact of datasets characteristics over aggregators' performance is investigated. Silhouette and imbalance coefficient are the most influential characteristics. Characteristics impact varies according to the specific scenario. Decision and regression trees are trained to guide the aggregator choice. Abstract: It is increasingly common applications where data are naturally generated in a distributed fashion, especially after the emergence of technologies like the Internet of Things (IoT). In sensor networks, in collaborative health or genomic projects, in credit risk analysis, among other domains, distinct features are collected from multiple sources, including the use of social media and mobile applications, and due to privacy concerns or communication costs, may not be shared among sites. This scenario of vertical data partitioning poses challenges to traditional machine learning (ML) approaches, as classical algorithms are designed to learn from the complete set of features. A common strategy is to combine predictions from local models trained at each site into a global model, and for this purpose, several aggregation methods have been proposed. In this work we tackle a gap within the related literature, performing a comparative evaluation of elementary and meta-learning-based aggregation methods to reveal their strengths and weakness for 46 datasets with varied characteristics. We show that no method outperforms its counterparts in all domains, emphasizing the need for experimental comparison to ensure a good choice in the domain of interest. Moreover, our experiments provide the first insights into the relations between datasets' properties and aggregators' performance. We show that for low class imbalance and a good instance-to-feature ratio, almost all aggregation methods tend to perform well. The silhouette coefficient (reflecting class separability) and class imbalance coefficient are the most influential properties on aggregators' performance, thus we recommend their analysis in the first step of the methodological design. We found that arithmetic-based methods are not suitable for datasets with poor class separability and a large number of classes, whereas meta-learning approaches are less sensitive for datasets with silhouette coefficient close to 0. Our analyses were summarized as classification and regression trees, which have the impact to serve as practical tools for future research. Taken together, our findings give rise to interesting applications in the domain of intelligent systems, especially regarding their potential to reduce the burden of vast experimental comparisons when training ML models with feature-partitioned data.