Selection of Dominant Features

Why we select data with smaller features from larger data sets ? By removing the most irrelevant and redundant features from the data, feature selection helps improve the performace of learning models by: 1. Alleviating the effect of the curse of dimensionality
2. Enhancing generalization capability 3. Speeding up learning process 4. Improving model interpretability

My approach to solve this issue is to observe their performance of normalized density distribution in discriminating each class: if the specific feature has obvious global pattern, I select it! According to famous Pareto principle saying: for many phenomena, 80% of the consequences stem from 20% of the causes I choose the dominant 9 features from original 55 ones, particularly, 3 of them are the most dominant due to their clear outward appearance. The other 6 features are less important than them. Thus, these sets with lower dimensionality will be basis for each classifiers.