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

497
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...
497
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.4K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.4K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

881
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
881
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

1.6K
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
1.6K
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

335
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
335
State Space Representation01:27

State Space Representation

734
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
734

You might also read

Related Articles

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

Sort by
Same author

VC-dimension of univariate decision trees.

IEEE transactions on neural networks and learning systems·2015
Same author

Design and analysis of classifier learning experiments in bioinformatics: survey and case studies.

IEEE/ACM transactions on computational biology and bioinformatics·2012
Same author

Ordering and finding the best of K > 2 supervised learning algorithms.

IEEE transactions on pattern analysis and machine intelligence·2006
See all related articles

Related Experiment Video

Updated: Apr 11, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.6K

Tree Ensembles on the Induced Discrete Space.

Olcay Taner Yildiz

    IEEE Transactions on Neural Networks and Learning Systems
    |May 27, 2015
    PubMed
    Summary

    K-forest, an extension of random forest, improves predictive accuracy by utilizing expanded feature spaces. This novel ensemble classifier significantly reduces error rates compared to standard random forests.

    Area of Science:

    • Machine learning
    • Ensemble methods
    • Data mining

    Background:

    • Decision trees are common predictive models in machine learning.
    • K-tree expands discrete feature spaces for improved decision tree performance.
    • K-tree's exponential complexity limits its practical application.

    Purpose of the Study:

    • To introduce K-forest, an ensemble classifier designed to overcome K-tree's limitations.
    • To evaluate the performance of K-forest against traditional random forests.
    • To reduce error rates in predictive modeling using an enhanced feature space.

    Main Methods:

    • K-forest is proposed as an extension of the random forest algorithm.
    • It involves selecting random subsets of features from an induced discrete feature space.

    More Related Videos

    Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
    06:56

    Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

    Published on: September 22, 2023

    1.8K
    Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
    12:26

    Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

    Published on: October 11, 2016

    13.9K

    Related Experiment Videos

    Last Updated: Apr 11, 2026

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

    6.6K
    Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
    06:56

    Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

    Published on: September 22, 2023

    1.8K
    Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
    12:26

    Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

    Published on: October 11, 2016

    13.9K
  • The induced discrete space is generated by considering orderings of k discrete attributes.

    • Main Results:

    • K-forest demonstrated a significantly lower error rate compared to standard random forests.
    • Simulations were conducted on 17 diverse datasets.
    • The proposed method shows superior predictive performance.

    Conclusions:

    • K-forest offers a computationally feasible and effective alternative to K-tree.
    • The ensemble approach leveraging expanded feature spaces enhances predictive accuracy.
    • This method provides a valuable advancement for machine learning classification tasks.