Sentence examples for common classifier decision from inspiring English sources

In this article, we use a common classifier, decision trees, to show that this approach allows us to identify similarities.

We also compared the SVM classifier with other machine learning classification methods, including linear discriminant classifier, decision trees, and artificial neural networks.

We have evaluated our proposed method with several common classifiers including naïve Bayes (NB), decision trees (DT), Gradient Boosting (GB), Deep Belief Networks (DBN) and D-TSK-FC.

We consider ensemble classification when there is no common labeled data for designing the function which aggregates classifier decisions.

In all ensemble classifiers decision tree is selected as base classifier.

Higher absolute values | s(x)| of scores s(x) indicate higher confidence about the classifiers decision.

Based on [ 32], a classifiers decision tree of RF is grown as follows.

In order to compare the performance of the FARM-based method with some well-known classifiers, Decision Tree, Random Forest, and Support Vector Machine (SVM) classifiers were trained.

Initially, each source of data is treated independently and subsequently all single-classifier decisions are combined using a multi-type classifier.

If the classifiers decisions are the same, k = 1 and k = 0 represent the case when the classifiers are entirely independent, and the agreement of the two classifiers equals that expected by chance [ 41].

The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999 2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier).