Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Survival Tree

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...

Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by

Same author

Warmer autumns and winters could reduce honey bee overwintering survival with potential risks for pollination services.

Scientific reports·2024

Same author

Inferring pesticide toxicity to honey bees from a field-based feeding study using a colony model and Bayesian inference.

Ecological applications : a publication of the Ecological Society of America·2021

Same author

Emergent Solutions to High-Dimensional Multitask Reinforcement Learning.

Evolutionary computation·2018

Same author

Counterfeit reagents: Fake serum has telltale fingerprint.

Nature·2017

Same author

Population growth of Varroa destructor (Acari: Varroidae) in commercial honey bee colonies treated with beta plant acids.

Experimental & applied acarology·2014

Same author

Classification as clustering: a Pareto cooperative-competitive GP approach.

Evolutionary computation·2010

Search research articles

Related Experiment Video

Updated: Jul 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Scaling genetic programming to large datasets using hierarchical dynamic subset selection.

Robert Curry, Peter Lichodzijewski, Malcolm I Heywood

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|August 19, 2007

Summary

New active learning algorithms, including cascaded and balanced block dynamic subset selection (DSS), significantly improve genetic programming (GP) classification accuracy and reduce training time without hardware. These methods enhance GP performance efficiently.

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jul 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Computational intelligence and machine learning.
Optimization algorithms and evolutionary computation.

Background:

Genetic programming (GP) faces computational overhead challenges, impacting its practical application.
Active learning, specifically subset selection heuristics like random (RSS) and dynamic (DSS), offers a software-based solution.

Discussion:

Introduces novel hierarchical dynamic subset selection (DSS) algorithms: RSS-DSS, cascaded RSS-DSS, and the previously undescribed balanced block DSS.
Evaluates these algorithms on large-scale, unbalanced real-world binary classification problems (30,000–500,000 exemplars).
Compares performance against GP without active learning and the original RSS-DSS algorithm.

Key Insights:

The cascaded and balanced block DSS algorithms significantly enhance classification accuracy compared to RSS-DSS.
These advanced DSS methods reduce the occurrence of degenerate solutions in GP.
GP training time is drastically reduced from hours to minutes without compromising classification performance.

Outlook:

These active learning strategies provide a viable approach to mitigate GP's computational demands.
Potential for broader application in complex machine learning tasks requiring efficient training.
Further research into adaptive and hierarchical subset selection for evolutionary algorithms.