Publications

@article{alaiz_rodriguez_information_2020,

title = {An information theoretic approach to quantify the stability of feature selection and ranking algorithms},

author = {Rocio Alaiz Rodriguez and Andrew C Parnell},

url = {https://www.sciencedirect.com/science/article/pii/S0950705120301593},

year  = {2020},

date = {2020-01-01},

journal = {Knowledge-Based Systems},

volume = {195},

pages = {105745},

abstract = {Feature selection is essential for high-dimensional data, but instability in algorithm outcomes can lead to varying feature rankings. This paper proposes using Jensen–Shannon divergence to measure the stability of feature selection methods, applicable to full and partial ranked lists. The approach emphasizes disagreements at the top of the list and offers desirable properties such as correction for change. The method is validated through experiments, including a food quality assessment, showing its effectiveness over traditional metrics like Spearman’s rank correlation.},

note = {Publisher: Elsevier},

keywords = {feature ranking, feature selection, Jensen-Shannon divergence, robustness, stability},

pubstate = {published},

tppubtype = {article}

}