Identifying quasi equally informative subsets in multi objective feature selection problems for classification

Date

2016-07-06

Author

Karakaya, Gülşah
Ahipaşaoğlu, Selin Damla
Taormina, Riccardo

Metadata

Show full item record

Item Usage Stats

57
views

0
downloads

URI

https://hdl.handle.net/11511/80990

Collections

Unverified, Conference / Seminar

Suggestions

OpenMETU
Core

Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems for Classification: A Max-Relevance Min-Redundancy Approach Karakaya, Gülşah; AHİPAŞAOĞLU, Selin Damla; TAORMİNA, Riccardo (2016-06-01) An emerging trend in feature selection is the development of two-objective algorithms that analyze the tradeoff between the number of features and the classification performance of the model built with these features. Since these two objectives are conflicting, a typical result stands in a set of Pareto-efficient subsets, each having a different cardinality and a corresponding discriminating power. However, this approach overlooks the fact that, for a given cardinality, there can be several subsets with sim...
Identifying Stability Properties of a Hybrid Spring–Mass–Damper via Piecewise LTI Approximation and Harmonic Transfer Functions Uyanık, İsmail; Ankaralı, Mustafa Mert; Cowan, Noah; Morgül, Ömer; Saranlı, Uluç (null; 2015-07-24)
Identifying preferred solutions in multiobjective combinatorial optimization problems Lokman, Banu (2019-01-01) We develop an evolutionary algorithm for multiobjective combinatorial optimization problems. The algorithm aims at converging the preferred solutions of a decision-maker. We test the performance of the algorithm on the multiobjective knapsack and multiobjective spanning tree problems. We generate the true nondominated solutions using an exact algorithm and compare the results with those of the evolutionary algorithm. We observe that the evolutionary algorithm works well in approximating the solutions in the...
Finding a representative nondominated set for multi-objective mixed integer programs Ceyhan, Gokhan; Koksalan, Murat; Lokman, Banu (2019-01-01) In this paper, we develop algorithms to find small representative sets of nondominated points that are well spread over the nondominated frontiers for multi-objective mixed integer programs. We evaluate the quality of representations of the sets by a Tchebycheff distance-based coverage gap measure. The first algorithm aims to substantially improve the computational efficiency of an existing algorithm that is designed to continue generating new points until the decision maker (DM) finds the generated set sat...
Finding preferred solutions under weighted Tchebycheff preference functions for multi-objective integer programs Karakaya, Gülşah; Köksalan, M. (2023-07-01) Many interactive approaches in multi-objective optimization assume the existence of an underlying preference function that represents the preferences of a decision maker (DM). In this paper, we develop the theory and an exact algorithm that guarantees finding the most preferred solution of a DM whose preferences are consistent with a Tchebycheff function for multi-objective integer programs. The algorithm occasionally presents pairs of solutions to the DM and asks which one is preferred. It utilizes the pre...

Citation Formats

G. Karakaya, S. D. Ahipaşaoğlu, and R. Taormina, “Identifying quasi equally informative subsets in multi objective feature selection problems for classification,” 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/80990.