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Keisuke Goto - One of the best experts on this subject based on the ideXlab platform.

  • data driven analysis of pareto set topology
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Naoki Hamada, Keisuke Goto
    Abstract:

    When and why can evolutionary multi-objective optimization (EMO) algorithms cover the entire Pareto set? That is a major concern for EMO researchers and practitioners. A recent theoretical study revealed that (roughly speaking) if the Pareto set forms a topological simplex (a curved line, a curved triangle, a curved tetrahedron, etc.), then decomposition-based EMO algorithms can cover the entire Pareto set. Usually, we cannot know the true Pareto set and have to estimate its topology by using the population of EMO algorithms during or after the runtime. This paper presents a data-driven approach to analyze the topology of the Pareto set. We give a theory of how to recognize the topology of the Pareto set from data and implement an algorithm to judge whether the true Pareto set may form a topological simplex or not. Numerical experiments show that the proposed method correctly recognizes the topology of high-dimensional Pareto sets within reasonable population size.

  • data driven analysis of pareto set topology
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Naoki Hamada, Keisuke Goto
    Abstract:

    When and why can evolutionary multi-objective optimization (EMO) algorithms cover the entire Pareto set? That is a major concern for EMO researchers and practitioners. A recent theoretical study revealed that (roughly speaking) if the Pareto set forms a topological simplex (a curved line, a curved triangle, a curved tetrahedron, etc.), then decomposition-based EMO algorithms can cover the entire Pareto set. Usually, we cannot know the true Pareto set and have to estimate its topology by using the population of EMO algorithms during or after the runtime. This paper presents a data-driven approach to analyze the topology of the Pareto set. We give a theory of how to recognize the topology of the Pareto set from data and implement an algorithm to judge whether the true Pareto set may form a topological simplex or not. Numerical experiments show that the proposed method correctly recognizes the topology of high-dimensional Pareto sets within reasonable population size.

Naoki Hamada - One of the best experts on this subject based on the ideXlab platform.

  • data driven analysis of pareto set topology
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Naoki Hamada, Keisuke Goto
    Abstract:

    When and why can evolutionary multi-objective optimization (EMO) algorithms cover the entire Pareto set? That is a major concern for EMO researchers and practitioners. A recent theoretical study revealed that (roughly speaking) if the Pareto set forms a topological simplex (a curved line, a curved triangle, a curved tetrahedron, etc.), then decomposition-based EMO algorithms can cover the entire Pareto set. Usually, we cannot know the true Pareto set and have to estimate its topology by using the population of EMO algorithms during or after the runtime. This paper presents a data-driven approach to analyze the topology of the Pareto set. We give a theory of how to recognize the topology of the Pareto set from data and implement an algorithm to judge whether the true Pareto set may form a topological simplex or not. Numerical experiments show that the proposed method correctly recognizes the topology of high-dimensional Pareto sets within reasonable population size.

  • data driven analysis of pareto set topology
    Genetic and Evolutionary Computation Conference, 2018
    Co-Authors: Naoki Hamada, Keisuke Goto
    Abstract:

    When and why can evolutionary multi-objective optimization (EMO) algorithms cover the entire Pareto set? That is a major concern for EMO researchers and practitioners. A recent theoretical study revealed that (roughly speaking) if the Pareto set forms a topological simplex (a curved line, a curved triangle, a curved tetrahedron, etc.), then decomposition-based EMO algorithms can cover the entire Pareto set. Usually, we cannot know the true Pareto set and have to estimate its topology by using the population of EMO algorithms during or after the runtime. This paper presents a data-driven approach to analyze the topology of the Pareto set. We give a theory of how to recognize the topology of the Pareto set from data and implement an algorithm to judge whether the true Pareto set may form a topological simplex or not. Numerical experiments show that the proposed method correctly recognizes the topology of high-dimensional Pareto sets within reasonable population size.

Wen-chih Chen - One of the best experts on this subject based on the ideXlab platform.

  • A novel data transformation model for small data-set learning
    International Journal of Production Research, 2016
    Co-Authors: I-hsiang Wen, Wen-chih Chen
    Abstract:

    In most highly competitive manufacturing industries, the sample sizes are usually very small in pilot runs, in order to quickly launch new products. However, it is always difficult for engineers to improve the quality in mass production runs based on the limited data obtained in this way. Past research has demonstrated that adding artificial samples can be an effective approach when learning with small data-sets. However, a prior analysis of the data is needed to deduce the appropriate sample distributions within which the artificial samples are generated. Johnson transformation is one of the well-known models that can be applied to bring data close to a normal distribution with the satisfaction of certain statistical assumptions. The sample size required for such data transformation methods is usually large, and this thus motivates the efforts of the current study to develop a new method which is suitable for small data-sets. Accordingly, this research proposes the small Johnson Data Transformation metho...

Chunhua Weng - One of the best experts on this subject based on the ideXlab platform.

  • Lifetime data set: single concept count
    2018
    Co-Authors: Michel Dumontier, George Hripcsak, Nicholas P. Tatonetti, Chunhua Weng
    Abstract:

    Single concept counts from the lifetime data set. 5,364,781 patients in this data set. File format: tab-delimited table; newline terminated. Columns: concept id - Unique numeric code identifying the concept; count - The number of patients with this concept in this data set; prevalence - The number of patients with this concept divided by the total number of patients in this data set (1.0 is 100%). The rows are arranged in ascending order by concept id

  • Lifetime data set: paired concept counts
    2018
    Co-Authors: Michel Dumontier, George Hripcsak, Nicholas P. Tatonetti, Chunhua Weng
    Abstract:

    Paired concept counts from the lifetime data set. 5,364,781 patients in this data set. File format: tab-delimited table; newline terminated. Columns: concept id 1 - Unique numeric code identifying the first of the paired concepts; concept id 2 - Unique numeric code identifying the second of the paired concepts; count - The number of patients with both concepts in this data set; prevalence - The number of patients with both concepts divided by the total number of patients in this data set (1.0 is 100%). Each unique pair of concepts has at most one row, i.e., the same two concepts do not appear in two separate rows. The rows are arranged in ascending order by concept id 1 and concept id 2. Concept id 1 is always the smaller numeric value

  • 5-year data set: paired concept counts
    2018
    Co-Authors: Michel Dumontier, George Hripcsak, Nicholas P. Tatonetti, Chunhua Weng
    Abstract:

    Paired concept counts from the 5-year data set. 1,790,431 patients in this data set. File format: tab-delimited table; newline terminated. Columns: concept id 1 - Unique numeric code identifying the first of the paired concepts; concept id 2 - Unique numeric code identifying the second of the paired concepts; count - The number of patients with both concepts in this data set; prevalence - The number of patients with both concepts divided by the total number of patients in this data set (1.0 is 100%). Each unique pair of concepts has at most one row, i.e., the same two concepts do not appear in two separate rows. The rows are arranged in ascending order by concept id 1 and concept id 2. Concept id 1 is always the smaller numeric value

  • 5-year data set: single concept count
    2018
    Co-Authors: Michel Dumontier, George Hripcsak, Nicholas P. Tatonetti, Chunhua Weng
    Abstract:

    Single concept counts from the 5-year data set. 1,790,431 patients in this data set. File format: tab-delimited table; newline terminated. Columns: concept id - Unique numeric code identifying the concept; count - The number of patients with this concept in this data set; prevalence - The number of patients with this concept divided by the total number of patients in this data set (1.0 is 100%). The rows are arranged in ascending order by concept id

I-hsiang Wen - One of the best experts on this subject based on the ideXlab platform.

  • A novel data transformation model for small data-set learning
    International Journal of Production Research, 2016
    Co-Authors: I-hsiang Wen, Wen-chih Chen
    Abstract:

    In most highly competitive manufacturing industries, the sample sizes are usually very small in pilot runs, in order to quickly launch new products. However, it is always difficult for engineers to improve the quality in mass production runs based on the limited data obtained in this way. Past research has demonstrated that adding artificial samples can be an effective approach when learning with small data-sets. However, a prior analysis of the data is needed to deduce the appropriate sample distributions within which the artificial samples are generated. Johnson transformation is one of the well-known models that can be applied to bring data close to a normal distribution with the satisfaction of certain statistical assumptions. The sample size required for such data transformation methods is usually large, and this thus motivates the efforts of the current study to develop a new method which is suitable for small data-sets. Accordingly, this research proposes the small Johnson Data Transformation metho...