We used the sequential forward floating selection (SFFS) to automatically select the combinations of the most relevant predictive features.
To reduce the number of features that were extracted, the feature selection algorithm of sequential forward floating selection (SFFS) was utilized to select the most significant features, with the selection criterion being the maximization of the average accuracy from a 10-fold cross-validation of a classification algorithm called classification and regression trees (CART).
More sophisticated methods, such as the sequential forward floating selection (SFFS) algorithm of Pudil et al. [13], include a backtracking phase after each addition where variables are successively eliminated if this improves the objective function.
Experimental setups comprises of the logistic regression (LR), decision tree (DT) and support vector machine (SVM) classifiers combined with forward selection (FS), backward selection (BS), sequential forward floating selection (SFFS), P value feature selection, minimum redundancy and maximum relevance feature selection (mRMR) techniques were utilised.
An adaptive version of such a forward floating selection method was applied here because of its low computational complexity and because it was capable of excluding the most prominent outliers during the sequential approximation process (Fig. 1).
The most relevant features for each model were chosen using sequential forward floating selection (SFFS).
We used sequential forward floating selection (SFFS) [ 36] which is a variation of a commonly adopted sequential forward selection (SFS) [ 37].
Search strategies include sequential forward selection [ 12], sequential backward selection [ 12], sequential floating selection [ 13], particle swarm optimization algorithm [ 14], genetic algorithm [ 15], ant colony algorithm [ 16], and breadth-first search [ 17].
If you can't, you probably need to anchor your floating selection.
To improve classification of microarray data, Zhou and Mao proposed SFS-LS bound and SFFS-LS bound algorithms for optimal gene selection by combining the sequential forward selection (SFS) and sequential floating forward selection (SFFS) with LS (Least Squares) bound measure [22].
