The results exhibit that integrating different feature scaling techniques could greatly enhance the classification accuracy of the classifiers.doi:10.1007/978-981-15-2693-0_14Siti-Farhana LokmanAbu Talib OthmanMuhamad Husaini Abu BakarShahrulniza Musa...
Feature scalingis a vital step in the preprocessing pipeline.1Decision treesand random forests are two of the very fewmachine learning algorithmswhere one does not need to worry about feature scaling. However, most of machine learning (e.g.: K-nearest neighbors) andoptimization algorithms(e.g.:...
These aren't exact requirements; we are only trying to speed things up. The goal is to get all input variables into roughly one of these ranges, give or take a few. Two techniques to help with this are feature scaling and mean normalization. Feature scaling involves dividing the input valu...
In (Shen and Shafiq2020), Shen and Shafiq proposed a complete feature engineering procedure by combining max–min scaling, polarizing for feature extension, RFE for feature selection, and PCA for dimensionality reduction; they tested their approach on 3,558 stocks from the Chinese stock market for...
or −0.5 ≤x(i)≤ 0.5 These aren't exact requirements; we are only trying to speed things up. The goal is to get all input variables into roughly one of these ranges, give or take a few. Two techniques to help with this are feature scaling and mean normalization. Feature scaling...
In the present techniques, as discussed in more detail below, the BNS scores preferably are used for feature scaling. It is noted that the foregoing definition of the BNS score generally is well-defined only for a binary feature (e.g., a feature indicating whether a particular word appears ...
(2021) also applied PCA together with feature scaling techniques, namely, standardization and min–max scaling, to find the optimal feature set from 40 technical indicators to predict the direction of seven stocks from the NYSE, NASDAQ, and NSE markets. Another study (Nabi et al.2019) applied...
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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,
3.6. Feature scaling If the values of the features in the machine learning algorithms are more similar to one another, there is a better chance that the algorithm will be trained well and quickly as opposed to a data set where the data points or feature values are highly dissimilar, which ...