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Related Concept Videos

Survival Tree01:19

Survival Tree

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 survival tree begins...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
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:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

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Related Experiment Video

Updated: May 23, 2026

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

[Data mining approach to cataclysmic variables candidates based on random forest algorithm].

Bin Jiang1, A-li Luo, Yong-heng Zhao

  • 1National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China. jiangbin@sdu.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|April 20, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient automatic method to identify cataclysmic variable (CV) candidates using random forest algorithms and template matching. The approach successfully discovered 16 new CV candidates, demonstrating feasibility for celestial body detection.

Related Experiment Videos

Last Updated: May 23, 2026

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

Area of Science:

  • Astronomy and Astrophysics
  • Computational Astrophysics

Context:

  • Automated analysis of astronomical spectra is crucial for discovering celestial objects.
  • Cataclysmic variables (CVs) are important binary star systems requiring efficient identification methods.

Purpose:

  • To develop an automatic and efficient method for identifying cataclysmic variable candidates.
  • To utilize machine learning, specifically random forest, for spectral classification and candidate selection.

Summary:

  • A novel method employs a random forest model trained on selected spectra to rank wavelengths and classify candidates.
  • Template matching is used for final candidate identification, with feedback incorporated to refine templates.
  • The approach successfully excluded non-candidates and identified 16 new cataclysmic variable candidates.

Impact:

  • Demonstrates a feasible and efficient approach for discovering celestial bodies, particularly CVs, within large astronomical datasets like LAMOST.
  • Provides a scalable method for astronomical surveys, potentially accelerating the discovery of variable celestial objects.