Sentence examples for multiple classifier experiments from inspiring English sources

MCCP combines multiple "classifier" experiments, which individually separate two protein groups incompletely, into a powerful super-ranking using a random forest (RF; Breiman, 2001) machine learning algorithm.

The usefulness of these methods to measure the difference between classifiers' decision space and their influence on recognition rate of multiple classifier systems are confirmed by a series of experiments.

Classifier selection is an important step in designing multiple classifier systems.

Multiple classifier systems combine several individual classifiers to deliver a final classification decision.

To utilize these features, a multiple classifier system was imported into this classifier.

These improvements indicate that when more classifiers are used in the multiple classifier system better improvement can be achieved.

Several methodologies exist for creating an ensemble classifier from individual classifiers; a survey on the design of multiple classifier systems can be found in [6].

In this paper we present a multiple classifier system (MCS) for on-line handwriting recognition.

In this paper, an approach to the automatic design of multiple classifier systems is proposed.

Cascade impactor results confirm the good performance of the multiple classifier concept.

For comparison, the reproducibility of all classifier experiments is plotted in Figure S1C.