Building Highly Reliable Quantitative Structure–Activity Relationship Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection

Dimensionality
reduction of the data set representation for the
construction of the quantitative structure–activity relationship
classification models is an important research subject for the interpretability
of the models and the computational cost efficiency of the classification
algorithms. Feature selection techniques are appropriate as only a
short number of relevant features should be used in the classification
process because irrelevant and redundant features should be discarded,
the same as the noninterpretable ones. In this paper, we propose an
embedded feature selection technique for the construction of classification
models using the rivality index neighborhood (RINH) algorithm. This
technique uses a filter selection in the preprocessing stage considering
the selectivity of the features as a selection criterion and a wrapper
technique in the processing stage based on the improvement of the
accuracy and reliability of the models generated using the RINH algorithm
with LTN and GTN functions. The results obtained using the RINH algorithm
with and without the selection of features and compared with those
results obtained using 14 machine learning algorithms have demonstrated
that the feature selection technique proposed in this paper is capable
of clearly building more accurate and reliable models, reducing the
data dimensionality around 90%, and generating high robust and interpretable
models.