Refaeilzadeh P, Tang L, Liu H (2007) On comparison of feature selection algorithms. In: Proceedings of AAAI workshop on evaluation methods for machine learning II, pp 34–39Refaeilzadeh, P., Tang, L., Liu, H.: On Comparison of Feature Selection Algorithms. In: Proceedings of AAAI ...
A Comparative Anal- ysis of Feature Selection Algorithms Based on Rough Set Theory, International Journal of Soft Computing, vol.1, no. 4, pp. 288-294, 2006.K. Thangavel, A. Pethalakshmi and P. Jaganathan. "A Comparative Analysis of Feature Selection Algorithms based on Rough Set Theory",...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Ba...
Despite of the intensive research and the large amount of works, the different kinds of feature selection algorithms have not been tested yet in the human activity recognition problem. It was the main motivation of our work and this paper tries to fill this gap. Therefore, in this article we...
Comparison in synthetic datasets In this section, we evaluate the performance of FS-RRC and other competitor algorithms on synthetic datasets. The results of the experiment on SD1 and SD2 are given in Table 2. Bold font represents an optimal feature subset without irrelevant and redundant features...
2. In the longitudinal direction, uncertainty measures concern two levels: decision class and classification, and the classification hierarchy eventually induces feature selection algorithms. The rest of this paper is organized as follows. Section 2 reviews NRSs and neighborhood self-information (NSI ...
Comparison of consensus algorithms Consensus algorithms are highly relevant to the performance, security, and scalability of blockchain systems. PoW20, the first blockchain consensus algorithm used, requires nodes joining the network to compute difficulty values to compete for block-out rights, thus gua...
Feature selection aims to reduce the dimensionality of patterns for clas-sificatory analysis by selecting the most informative rather than irrelevant and/or redundant features. In this study, a hybrid genetic algorithm for feature selection is presented to combine the advantages of both wrappers and ...
Comparison of feature importance measures as explanations for classification models. SN Appl Sci. 2021;3:02. Article Google Scholar Ribeiro MT, Singh S, Guestrin C. Anchors: High-precision model-agnostic explanations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32. ...
The second incorporates the selection, crossover, and mutation techniques of genetic algorithms for greater diversity. Both algorithms are fitted with objective functions obtained from stakeholder requirements for multiple objective feature selection. Empirical evaluation is conducted with stakeholder requirements...