Voting-based ensemble learning for partial lexicographic preference forests over combinatorial domains

Li, Xudong; Truszczyński, Mirosław

doi:10.1007/s10472-019-09645-7

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Liu and Truszczynski [110] presented a method for ensemble learning that depends on merging a set of small trees (partial lexicographic preference (PLP) trees). Instead of using a large tree.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.

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Section: Related Workmentioning

confidence: 99%

Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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“…In addition to the quantitative measures, we qualitatively assess cluster quality by constructing KNNgraphs (with K = 10) from the distance matrix and coloring vertices according to each algorithm's cluster assignments. To test PlpDis-powered clustering, we applied the selected algorithms to the task of clustering PLP-forests learned from a Car Evaluation dataset 1 using Liu and Truszczynski's greedy algorithm (Liu and Truszczynski 2019). In the experiment, we learned forests of sizes 100, 500, 1,000, 2,500, 5,000, and 10,000 with each tree learning from 100 examples.…”

Section: Clustering Plp-treesmentioning

confidence: 99%

Clustering Partial Lexicographic Preference Trees (Student Abstract)

Allen

Umapathy

et al. 2021

AAAI

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In this work, we consider distance-based clustering of partial lexicographic preference trees (PLP-trees), intuitive and compact graphical representations of user preferences over multi-valued attributes. To compute distances between PLP-trees, we propose a polynomial time algorithm that computes Kendall's Tau distance directly from the trees and show its efficacy compared to the brute-force algorithm. To this end, we implement several clustering methods (i.e., spectral clustering, affinity propagation, and agglomerative nesting) augmented by our distance algorithm, experiment with clustering of up to 10,000 PLP-trees, and show the effectiveness of the clustering methods and visualizations of their results.

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Probabilistic Lexicographic Preference Trees

Truszczyński

2021

Algorithmic Decision Theory

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Voting-based ensemble learning for partial lexicographic preference forests over combinatorial domains

Cited by 4 publications

References 15 publications

Ensemble learning based on relative accuracy approach and diversity teams

Ensemble learning based on relative accuracy approach and diversity teams

Clustering Partial Lexicographic Preference Trees (Student Abstract)

Probabilistic Lexicographic Preference Trees

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