Sentence examples for multiple classifiers in from inspiring English sources

Consensus clustering methods are motivated by the success of combining multiple classifiers in many areas.

However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood.

Our experimental study also shows that the infection state estimation can be improved by combining multiple classifiers in networks with high degree skewness such as OREGON.

In this paper, the iterative Boolean combination (IBC) technique is proposed for efficient fusion of the responses from multiple classifiers in the ROC space.

Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures.

Moreover, both MC-ELM-AE-TsEL-W and B-MC-ELM-AE-TsEL-W outperform the majority-voting-based MC-ELM-AE-TsEL-V and B-MC-ELM-AE-TsEL-V, which indicates that the weighted-voting is more effective to boost multiple classifiers in this work.

We consider small sample effects, specific only to multiple classifiers system design in the two-category case of two important fusion rules: (1) linear weighted average (weighted voting), realized either by the standard Fisher classifier or by the single-layer perceptron, and (2) the non-linear Behavior-Knowledge-Space method.

By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets.

In an effort to improve classification accuracy, it is often desired to incorporate the results from multiple classifiers that are varied in terms of their approach or algorithm.

Furthermore, the results prompted a novel design of multiple classifier systems in which selection and fusion are recurrently applied to a population of best combinations of classifiers rather than the individual best.

In order to overcome the disadvantages of the common fusion method by parallel structure, a hierarchical propelled strategy of multiple classifiers fusion (HPSMCF) is proposed in this paper.