A new approach to ensemble learning is introduced that takes ranking rather than classification as fundamental, leading to models on the symmetric group and its cosets. The approach uses a generalization of the Mallows model on permutations to combine multiple input rankings. Applications include the task of combining the output of multiple search engines and multiclass or multilabel classifica...

In this paper, we develop a principled framework for efficient processing of ad-hoc top-k (ranking) aggregate queries in OLAP. Such queries provide the k groups with the highest aggregates to decision makers. Essential support of top-k aggregate queries is lacking in current RDBMSs, which process such queries in a naı̈ve and overkill materialize-group-sort scheme, therefore can be prohibitively ...

We describe a system for automatically ranking documents by degree of militancy, designed as a tool both for finding militant websites and prioritizing the data found. Our ranking system employs a small hand-selected vocabulary based on group membership markers used by insiders to identify members and member properties (us) and outsiders and threats (them). We use the same vocabulary to build a...

Infeasible individuals are often underrated when evolutionary algorithms are used for solving constraint optimization problems. This paper proposes a new approach to balance the feasible and infeasible individuals. The population is divided into two groups: feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. Parents for reproduction are ...