Dynamic Feature Selection Based on Pareto Front Optimization
Jhoseph Jesus, Anne M. P. Canuto, Daniel Sabino Amorim de Araújo · 2018
One of the main issues of machine learning algorithms is the curse of dimensionality. With the fast growing of complex data in real world scenarios, the feature selection becomes a mandatory preprocessing step in any application to reduce both the complexity of the data and the computing time. Based on that, several works have been produced in order to develop efficient methods to perform this task. Most feature selection methods select the best attributes based on some specific criteria. Additionally, recent studies have successfully constructed models to select features considering the particularities of the data, assuming that similar samples should be treated separately. Although some advance has been made, a bad choice of one single criteria to evaluate the importance of the attributes and the arbitrary choice of the number of features made by the user can lead to a poor analysis. In order to overcome some of these issues, this work brings an improvement of a dynamic feature selection algorithm (DFS) by using the idea of pareto front multi-objective optimization, which allow us to both consider distinct perspectives of the features relevance and automatically set the number of attributes to select. We tested our approach using 15 artificial and real world data and results have shown that when compared to the original DFS method, the performance of the proposed method is remarkable superior. In fact, the results are very promising since the proposed method also achieved better performance than well-established dimensionality reduction methods and when using the original datasets, showing that the reduction of noisy and/or redundant attributes can have a positive effect in the performance of a classification task.