Sentence examples for decision fusion classification from inspiring English sources

In order to demonstrate the effects of artifacts on classifier performance, a series of classification experiments are designed using a multi-channel decision fusion classification algorithm.

A multiple kNN classifier based decision level fusion classification architecture is proposed for multimodal speech prosody and vocal source expert fusion.

When a large number of multimodal sensors are available in the environment, a common way to perform activity recognition is to rely on the global decision fusion of individual classification performed on the sensor nodes (ensemble classifiers [59]).

The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery.

By using a weighted decision fusion method, the overall classification accuracy was higher than any of the individual channels.

Additionally, it is shown that the C-T estimates can be used to implement various forms of minimum-distance classifiers for individual channels and for single-channel heterogeneous, multi-channel homogeneous, and multi-channel heterogeneous homogenous ERP classification through decision fusion.

This work includes three important parts: Feature Extracting, Classification, and Decision Fusion.

Table 3 Classification results of pregnancy and labor contractions for each channel for the test data by using a weighted decision fusion rule   Weighted decision fusion method (in %) Classification accuracy of pregnancy contractions 95.2 Classification accuracy of labor contractions 89.3 Overall classification accuracy 92.4.

The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers.

Two kinds of methods for feature fusion are used to improve classification accuracy: combination fusion and decision fusion.

As a result, when a decision fusion rule was applied, an improved accuracy of the classification decision compared to a decision based on any of the individual data sources alone was obtained.