Weighted Feature Selection Method for Improving Decisions in Milling Process Diagnosis

Zajdel, Roman; Kusy, Maciej; Kluska, Jacek; Żabiński, Tomasz

doi:10.1007/978-3-030-61401-0_27

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following research is worth mentioning here: combinatorial fusion analysis (Li et al, 2013), a voting scheme combination framework (Rokach et al, 2006) or aggregation function based feature ranking (Pes, 2020). The idea of fusion of various FS methods has also been investigated by the authors of the current work (Kusy et al, 2020;Zajdel et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…This is because the conducted selection of features is based both on the accuracy of the classifier and the application of filtering methods. This paper can be treated as an extension of the work presented by Kusy et al (2020) and Zajdel et al (2020). However, its main contribution is the introduction of a novel weighted combined ranking score that allows for selecting the most significant features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A weighted wrapper approach to feature selection

Kusy

Zajdel

2021

View full text Add to dashboard Cite

This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson's linear correlation coefficient, the ReliefF algorithm and decision trees. A new wrapper method is proposed which, on the basis of a fusion of the above approaches and the performance of a classifier, is capable of creating a distinct, ordered subset of attributes that is optimal based on the criterion of the highest classification accuracy obtainable by a convolutional neural network. The introduced feature selection uses a weighted ranking criterion. In order to evaluate the effectiveness of the solution, the idea is compared with sequential feature selection methods that are widely known and used wrapper approaches. Additionally, to emphasize the need for dimensionality reduction, the results obtained on all attributes are shown. The verification of the outcomes is presented in the classification tasks of repository data sets that are characterized by a high dimensionality. The presented conclusions confirm that it is worth seeking new solutions that are able to provide a better classification result while reducing the number of input features.

show abstract