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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.
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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.
Diverse classifiers viz.
features extracted from the data stream of multiple, diverse sensors.
These burdens are multiple, diverse and partly invisible [1].
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.
The prospective benefits of employing multiple diverse sensors outweigh the potential limitations.
They provide multiple, diverse opportunities to come to life, together.
EBP1 appears to fulfill multiple diverse biological functions.
A best essentiality classifier is obtained by combining the outputs of these diverse classifiers.
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