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Diverse classifiers viz.
Therefore, the fundamental need for methods aimed to design "accurate and diverse" classifiers is currently acknowledged.
Given an initial large set of classifiers, our approach is aimed at selecting the subset made up of the most accurate and diverse classifiers.
To avoid knowledge corruption and sustain a high level of accuracy over time, an adaptive multiclassifier system (AMCS) may integrate information from diverse classifiers that are guided by a population-based evolutionary optimization algorithm.
To provide a high level of accuracy over time while minimizing the computational complexity, the AMCS integrates information from multiple diverse classifiers, where learning is guided by an aggregated dynamical niching PSO (ADNPSO) algorithm that optimizes networks according both these objectives.
A best essentiality classifier is obtained by combining the outputs of these diverse classifiers.
Similar(54)
Error-correcting output codes (ECOC) are used to design diverse classifier ensembles.
The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers' outputs to obtain more precise stock return and risk predictions.
The computational methods represent a broad category of predicting methods for DNA-binding proteins, known to be highly diverse in classifiers and protein representation.
The more accurate each base classifier and the more diverse different base classifiers, the better the classification performance of the ensemble learning.
Followed by: "Diverse".
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