Feature Selection MethodsFeature Selection AlgorithmsFeature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the ...
In the context of high-dimensional credit card fraud data, researchers and practitioners commonly utilize feature selection techniques to enhance the performance of fraud detection models. This study presents a comparison in model performance using the m
Nature-inspired algorithms (NIA) are proven to be the potential tool for solving intricate optimization problems and aid in the development of better computational techniques. In recent years, these algorithms have raised considerable interest to optimize feature selection problems. In literature, NIA is...
This study delves into a comparison between two feature selection methods: Shapley Additive exPlanation (SHAP)-value-based selection [3] and commonly used importance-based selection [4,5]. SHAP leverages game theory concepts to compute feature importance in two steps: training a classification model ...
Check access to the full text by signing in through your organization. Access through your organization Section snippets Feature selection Feature selection majorly focuses on selecting a subset of features from the input data, which could effectively describe the input data. On the other hand, ...
Toward integrating feature selection algorithms for classification and clustering IEEE Transactions on Knowledge and Data Engineering, 17 (2005), pp. 491-502 View in ScopusGoogle Scholar Meyer and Bontempi, 2006 Meyer P.E., Bontempi G. On the use of variable complementarity for feature selection in...
In addition to feature pre-selection based on drug properties and biological relevance, we also evaluated automated feature selection algorithms in application to genome-wide expression data. We used two techniques, based on linear and non-linear methods. First, stability selection, which uses lasso ...
Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models Mahbod Issaiy Diana Zarei David S. Liebeskind Journal of Neurology(2025) LLpowershap: logistic loss-based automated Shapley values feature selection method ...
Borutais a feature ranking and selection algorithm that was developed at the University of Warsaw. This algorithm is based on random forests, but can be used on XGBoost and different tree algorithms as well. At Fiverr, I used this algorithm with some improvements to XGBoost ranking and classifie...
Therefore, performing feature selection over high-dimensional data streams is still an open research topic since the majority of existing feature selection algorithms require multiple passes over data (Barddal et al., 2017). In addition to feature drifts, our proposal is also tailored for tackling ...