Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
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...
5.3K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
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...
4.0K
Decision Making01:20

Decision Making

85
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...
85
Survival Tree01:19

Survival Tree

57
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
57
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.0K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.0K
Heuristics01:21

Heuristics

70
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
70

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Exploiting Data Distribution: A Multi-Ranking Approach.

Entropy (Basel, Switzerland)·2025
Same author

Importance of Characteristic Features and Their Form for Data Exploration.

Entropy (Basel, Switzerland)·2024
Same author

Selected Data Mining Tools for Data Analysis in Distributed Environment.

Entropy (Basel, Switzerland)·2023
Same author

Improved EAV-Based Algorithm for Decision Rules Construction.

Entropy (Basel, Switzerland)·2023
Same author

Decision Rules Derived from Optimal Decision Trees with Hypotheses.

Entropy (Basel, Switzerland)·2021
Same author

Decision Rules Construction: Algorithm Based on EAV Model.

Entropy (Basel, Switzerland)·2020

相关实验视频

Updated: May 31, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

贪的算法用于从决策树组合中导出决策规则.

Evans Teiko Tetteh1, Beata Zielosko1

  • 1Institute of Computer Science, University of Silesia in Katowice, Bȩdzińska 39, 41-200 Sosnowiec, Poland.

Entropy (Basel, Switzerland)
|January 24, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种贪的算法,用于从决策树集合中提取决策规则,提高分布式数据设置中的模型解释性和准确性. 该方法通过生成更短,更准确的规则来增强知识发现.

关键词:
决策规则 决策规则 决策规则决策树 决策树 是一个决定树.总的来说,一个团队就是一个团队.贪的算法 贪的算法长度 长度 长度 长度 长度 长度

更多相关视频

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

5.9K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

相关实验视频

Last Updated: May 31, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

5.9K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.4K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 决策规则为知识提取和决策提供了透明度.
  • 传统的算法,如CART和ID3,从数据中构建决策树.

研究的目的:

  • 引入一个贪的算法来从决策树合集中推导决策规则.
  • 在分布式数据环境中增强可解释性和概括性.

主要方法:

  • 在代表分布式数据的引导数据集上使用CART和ID3诱导决策树.
  • 应用一个贪的算法来导出在多个决策树上一致的决策规则.

主要成果:

  • 增加参数α (0≤α<1) 的值会导致更短的决策规则.
  • 拟议的方法可以提高基于规则的模型的分类准确性.

结论:

  • 贪的算法有效地从集合中提取可解释和准确的决策规则.
  • 这种方法有利于在分布式环境中对知识的表示和发现.