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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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FeatureSelect: a software for feature selection based on machine learning approaches.

Yosef Masoudi-Sobhanzadeh1, Habib Motieghader1, Ali Masoudi-Nejad2

  • 1Laboratory of system Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.

BMC Bioinformatics
|April 5, 2019
PubMed
Summary
This summary is machine-generated.

FeatureSelect is a new software application for feature selection, utilizing wrapper methods for improved performance over traditional filter methods. It offers user-friendly gene selection with various algorithms and statistical measurements.

Keywords:
ClassificationFeature selectionGene selectionMachine learningRegression

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Data Science

Background:

  • Feature selection is a critical preprocessing step in diverse scientific fields.
  • Existing tools often rely on filter methods, which generally show lower performance compared to wrapper methods.
  • There is a need for more effective and versatile feature selection tools.

Purpose of the Study:

  • Introduce FeatureSelect, a novel software application for feature and gene selection.
  • Address the performance limitations of filter-based methods by incorporating wrapper-based approaches.
  • Provide a user-friendly platform for applying various feature selection algorithms to diverse datasets.

Main Methods:

  • Developed the FeatureSelect software application.
  • Implemented optimization algorithms, including WCC (Wrapper Correlation Coefficient), and three types of learners.
  • Integrated 10 efficient, well-known, and recently developed algorithms.
  • Applied FeatureSelect to various datasets, evaluating algorithm performance using metrics like accuracy, sensitivity, and specificity.

Main Results:

  • FeatureSelect successfully implements both filter and wrapper methods for feature selection.
  • Performance of algorithms varied across different datasets, with WCC, LCA, FOA, and LA showing overall suitability.
  • Empirical results confirmed the superiority of wrapper methods over filter methods for feature selection tasks.
  • The software provides comparison diagrams and statistical measurements for analysis.

Conclusions:

  • FeatureSelect is a powerful, open-source feature and gene selection software based on wrapper methods.
  • It offers a user-friendly interface and a comprehensive suite of algorithms for diverse research applications.
  • The software is freely available on GitHub under an MIT license.