Combining Pattern Classifiers: Methods and Algorithms

By Subhash C. Bagui. Technometrics, 2005

4,300 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7a92314ff9ef62aab0f30510225736aa30aa7b84

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Statistics and Probability

Citations in the corpus

Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information (2014, methodology): “MLA architecture and the statistical distributions of observed data guides the training of classification models, which is usually carried out by minimizing a loss (error) function (Kuncheva, 2004; Marsland, 2009).”
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, methodology): “It has been shown that classifier Ensembles have great potential to outperform traditional single classifier systems especially on challenging recognition tasks [Kuncheva 2004].”
Ensemble learning: A survey (2018, background): “Majority voting of the inducers ’ predictions is performed to determine the final prediction of an unseen instance (Kuncheva, 2004).”
Evaluation of Classification Algorithms Using MCDM and Rank Correlation (2012, background): “Kappa statistic (KapS): This is a classifier performance measure that estimates the similarity between the members of an ensemble in multi-classifiers systems.(21) KapS 1⁄4 PðAÞ PðEÞ 1 PðEÞ : P(A) is the accuracy of the classifier and P(E) is the probability that agreement among classifiers is due t”
Data-driven Soft Sensors in the process industry (2009, methodology): “Comprehensive reviews of ensemble building techniques were published in Kuncheva (2004) to improve the model's prediction performance.”
A survey of multiple classifier systems as hybrid systems (2014, methodology): “According to [22,38] we can enforce the diversity of a classifier pool by the manipulation of either individual classifier inputs, outputs, or models.”
A Review of Ensemble Methods in Bioinformatics (2010, background): “However, this is often achieved at the expense of increased model complexity (decreased model interpretability) [13].”