Supervised Rank Aggregation Research Articles

Supervised Rank Aggregation (SRA): A Novel Rank Aggregation Approach for Ensemble-based Feature Selection

Background: Feature selection (FS) is critical for high dimensional data analysis. Ensemble based feature selection (EFS) is a commonly used approach to develop FS techniques. Rank aggregation (RA) is an essential step in EFS where results from multiple models are pooled to estimate feature importance. However, the literature primarily relies on static rule-based methods to perform this step which may not always provide an optimal feature set. The objective of this study is to improve the EFS performance using dynamic learning in RA step. Method: This study proposes a novel Supervised Rank Aggregation (SRA) approach to allow RA step to dynamically learn and adapt the model aggregation rules to obtain feature importance.Method: This study proposes a novel Supervised Rank Aggregation (SRA) approach to allow RA step to dynamically learn and adapt the model aggregation rules to obtain feature importance. Results: We evaluate the performance of the algorithm using simulation studies and implement it into real research studies, and compare its performance with various existing RA methods. The proposed SRA method provides better or at par performance in terms of feature selection and predictive performance of the model compared to existing methods. Conclusion: SRA method provides an alternative to the existing approaches of RA for EFS. While the current study is limited to the continuous cross-sectional outcome, other endpoints such as longitudinal, categorical, and time-to-event data could also be used.

Is Rank Aggregation Effective in Recommender Systems? An Experimental Analysis

Recommender Systems are tools designed to help users find relevant information from the myriad of content available online. They work by actively suggesting items that are relevant to users according to their historical preferences or observed actions. Among recommender systems, top- N recommenders work by suggesting a ranking of N items that can be of interest to a user. Although a significant number of top- N recommenders have been proposed in the literature, they often disagree in their returned rankings, offering an opportunity for improving the final recommendation ranking by aggregating the outputs of different algorithms. Rank aggregation was successfully used in a significant number of areas, but only a few rank aggregation methods have been proposed in the recommender systems literature. Furthermore, there is a lack of studies regarding rankings’ characteristics and their possible impacts on the improvements achieved through rank aggregation. This work presents an extensive two-phase experimental analysis of rank aggregation in recommender systems. In the first phase, we investigate the characteristics of rankings recommended by 15 different top- N recommender algorithms regarding agreement and diversity. In the second phase, we look at the results of 19 rank aggregation methods and identify different scenarios where they perform best or worst according to the input rankings’ characteristics. Our results show that supervised rank aggregation methods provide improvements in the results of the recommended rankings in six out of seven datasets. These methods provide robustness even in the presence of a big set of weak recommendation rankings. However, in cases where there was a set of non-diverse high-quality input rankings, supervised and unsupervised algorithms produced similar results. In these cases, we can avoid the cost of the former in favor of the latter.

Supervised rank aggregation based on query similarity for document retrieval

This paper is concerned with supervised rank aggregation, which aims to improve the ranking performance by combining the outputs from multiple rankers. However, there are two main shortcomings in previous rank aggregation approaches. First, the learned weights for base rankers do not distinguish the differences among queries. This is suboptimal since queries vary significantly in terms of ranking. Besides, most current aggregation functions do not directly optimize the evaluation measures in ranking. In this paper, the differences among queries are taken into consideration, and a supervised rank aggregation function is proposed. This aggregation function is directly optimizing the evaluation measure NDCG, referred to as RankAgg.NDCG, We prove that RankAgg.NDCG can achieve better NDCG performance than the linear combination of the base rankers. Experimental results performed on benchmark datasets show our approach outperforms a number of baseline approaches.