Sentence examples for backward feature selection method from inspiring English sources
As it can be seen in Table 2, the LR based classification setups combined with backward feature selection method (smoker, number of years smoking, age, diabetes type and raised cholesterol) were able to classify the RACPC patient dataset with a classification accuracy of 68.99 %.
In the elimination stage, we use a backward feature selection method.
Furthermore, more suitable backward feature selection method needs to be exploited so that the gene clustering and cluster selection processes can be integrated better.
Noshadi proposed an algorithm which combines Lempel Ziv with EMD for feature extraction on the EEG signal, using t-test and a forward or backward feature selection method [ 6].
We used a backward stepwise feature selection method that utilizes the alignment to the HMM profiles based on recursive feature elimination (RFE) algorithm, termed HMM RFE (Guyon et al., 2002).
Table 7 Results of evaluating feature selection methods Feature selection method F-measure Recall Precision Accuracy Backward elimination 99.31 77.78 95.45 85.71 Forward selection 98.22 83.33 22.77 35.71.
Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted.
Seventeen features are selected from total 100 features using the forward feature selection method.
Second, a backward feature selection approach based on linear-kernel SVM was used to selected drug response-relevant features instead of a screening scheme by CCLE and CGP.
To keep the loss of information to a minimum we tested the correlation-based feature selection method and the consistency feature selection method with several searches: the forward search, the backward search, the bidirectional search, the greedy search, and feature ranking methods.
This is because the bidirectional search algorithm (feature selection method) obtains a set of representative features that is achieved by iteratively applying sequential forward and backward selections to select good and remove bad features from the feature set, and further provides a solution that is close to optimal.
