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A Survey of Adaptive Sorting Algorithms
, 1992
"... Introduction and Survey; F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems  Sorting and Searching; E.5 [Data]: Files  Sorting/searching; G.3 [Mathematics of Computing]: Probability and Statistics  Probabilistic algorithms; E.2 [Data Storage Represe ..."
Abstract

Cited by 65 (3 self)
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Introduction and Survey; F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems  Sorting and Searching; E.5 [Data]: Files  Sorting/searching; G.3 [Mathematics of Computing]: Probability and Statistics  Probabilistic algorithms; E.2 [Data Storage Representation]: Composite structures, linked representations. General Terms: Algorithms, Theory. Additional Key Words and Phrases: Adaptive sorting algorithms, Comparison trees, Measures of disorder, Nearly sorted sequences, Randomized algorithms. A Survey of Adaptive Sorting Algorithms 2 CONTENTS INTRODUCTION I.1 Optimal adaptivity I.2 Measures of disorder I.3 Organization of the paper 1.WORSTCASE ADAPTIVE (INTERNAL) SORTING ALGORITHMS 1.1 Generic Sort 1.2 CookKim division 1.3 Partition Sort 1.4 Exponential Search 1.5 Adaptive Merging 2.EXPECTEDCASE ADAPTIV
Unsupervised Rank Aggregation with DistanceBased Models
"... The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they require domain knowledge or supervised ranked data, both of which are expensive to acquire. In order to ..."
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Cited by 16 (6 self)
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The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they require domain knowledge or supervised ranked data, both of which are expensive to acquire. In order to address these limitations, we propose a mathematical and algorithmic framework for learning to aggregate (partial) rankings without supervision. We instantiate the framework for the cases of combining permutations and combining topk lists, and propose a novel metric for the latter. Experiments in both scenarios demonstrate the effectiveness of the proposed formalism. 1.
ClassicalSorting Embedded in Genetic Algorithms for Improved Permutation Search
, 2000
"... A sorting algorithm defines a path in the search space of n! permutations based on the information provided by a comparison predicate. Our generic mutation operator for hybridization, blends a hillclimber and follows the path traced by any sorting algorithm. Our proposal adds search (exploitation) ..."
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A sorting algorithm defines a path in the search space of n! permutations based on the information provided by a comparison predicate. Our generic mutation operator for hybridization, blends a hillclimber and follows the path traced by any sorting algorithm. Our proposal adds search (exploitation) capability to the mutation operator. Mutation requests swaps to construct and test new permutations, while the sorting algorithm supplies suggestions for swapping pairs as comparison to perform. The need to compare pairs of items in sorting is fulfilled by evaluating a guiding function. This new HGAsorting, hybrid instantiated with Insertion Sort, dramatically improves previous results for a benchmark of experiments of the ErrorCorrecting Graph Isomorphism.
A Framework for . . .
, 2008
"... The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they generally require either domain knowledge or supervised ranked data, both of which are expensive to acqu ..."
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The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they generally require either domain knowledge or supervised ranked data, both of which are expensive to acquire. To address these limitations, we propose 1 a mathematical and algorithmic framework for learning to aggregate (partial) rankings in an unsupervised setting, and instantiate it for the cases of combining permutations and combining topk lists. Furthermore, we also derive an unsupervised learning algorithm for rank aggregation (ULARA), which approximates the behavior of this framework by directly optimizing the weighted Borda count. We experimentally demonstrate the effectiveness of both approaches on the data fusion task.