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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

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Related Experiment Videos

Classification of soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their

Ömer Karakoç1,2, Samet Memiş3, Bahar Sennaroglu1

  • 1Department of Industrial Engineering, Marmara University, İstanbul, Türkiye.

Plos One
|May 13, 2026
PubMed
Summary

This study benchmarks 35 soft decision-making (SDM) algorithms using fuzzy parameterized fuzzy soft matrices (fpfs-matrices). Several SDM methods demonstrated competitive performance in machine learning classification tasks involving uncertainty.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Fuzzy parameterized fuzzy soft matrices (fpfs-matrices) provide a robust framework for uncertainty modeling.
  • A gap exists in large-scale comparative studies of soft decision-making (SDM) algorithms derived from fpfs-matrices within machine learning.

Purpose of the Study:

  • To comprehensively evaluate and classify 35 SDM algorithms based on fpfs-matrices.
  • To benchmark these algorithms on diverse datasets using standard classification metrics.
  • To identify top-performing algorithms and provide practical guidance for selecting SDM methods.

Main Methods:

  • Utilized the Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC).
  • Evaluated 35 SDM algorithms across ten UCI Machine Learning Repository datasets.
  • Assessed performance using accuracy, precision, recall, specificity, and F1-score, with statistical validation via Friedman and Nemenyi tests.

Main Results:

  • SDM methods employing fpfs-matrices show competitive classification performance in uncertain environments.
  • Identified top-performing algorithms: A19 (Rank 1), YHX14 (Rank 2), and a tie for Rank 3 (VMH16, AKO18o, A19/2).
  • Statistical tests confirmed the significance of performance differences.

Conclusions:

  • SDM methods based on fpfs-matrices are effective for machine learning classification tasks with uncertainty.
  • The study offers a decision-support framework and practical insights for algorithm selection.
  • This research serves as a theoretical reference for future work in uncertainty-based decision-making.