Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Survival Tree01:19

Survival Tree

196
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...
196
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

28.6K
The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
28.6K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

27.0K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
27.0K
The Availability Heuristic01:08

The Availability Heuristic

6.7K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
6.7K
Heuristics01:21

Heuristics

208
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...
208
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.5K
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...
4.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Systematic Review of Diffusion Models for Medical Image-Based Diagnosis: Methods, Taxonomies, Clinical Integration, Explainability, and Future Directions.

Diagnostics (Basel, Switzerland)·2026
Same author

Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes.

Entropy (Basel, Switzerland)·2024
Same author

On Complexity of Deterministic and Nondeterministic Decision Trees for Conventional Decision Tables from Closed Classes.

Entropy (Basel, Switzerland)·2023
Same author

Selected Data Mining Tools for Data Analysis in Distributed Environment.

Entropy (Basel, Switzerland)·2023
Same author

Applications of Depth Minimization of Decision Trees Containing Hypotheses for Multiple-Value Decision Tables.

Entropy (Basel, Switzerland)·2023
Same author

Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis.

Diagnostics (Basel, Switzerland)·2023

Related Experiment Video

Updated: Oct 30, 2025

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

7.7K

Entropy-Based Greedy Algorithm for Decision Trees Using Hypotheses.

Mohammad Azad1, Igor Chikalov2, Shahid Hussain3

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia.

Entropy (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel greedy algorithm using entropy for constructing decision trees with advanced attribute queries. Experiments show its effectiveness on diverse datasets and Boolean functions.

Keywords:
decision treeentropygreedy algorithmhypothesis

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.6K
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

6.2K

Related Experiment Videos

Last Updated: Oct 30, 2025

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

7.7K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.6K
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

6.2K

Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Decision trees are fundamental in machine learning for classification and regression.
  • Existing decision tree algorithms primarily use single-attribute queries.
  • Exact learning paradigms explore more complex query types, including those involving multiple attributes.

Purpose of the Study:

  • To introduce and evaluate decision trees capable of utilizing both single-attribute and hypothesis-based queries.
  • To develop a greedy algorithm for constructing these advanced decision trees.
  • To assess the performance of the proposed method on various datasets.

Main Methods:

  • A greedy algorithm based on entropy is presented for decision tree construction.
  • The algorithm incorporates queries based on single attributes and hypotheses of all attribute values.
  • Computer experiments were conducted on diverse datasets and randomly generated Boolean functions.

Main Results:

  • The proposed greedy algorithm successfully constructs decision trees using both conventional and hypothesis-based queries.
  • Experimental results demonstrate the algorithm's performance across various data types.
  • The study validates the utility of the entropy-based approach for these advanced decision trees.

Conclusions:

  • The developed greedy entropy-based algorithm is effective for constructing decision trees with complex query capabilities.
  • The findings contribute to the field of exact learning and decision tree induction.
  • Further research can explore optimizations and applications of these advanced decision trees.