bernoulli naive bayes (Syrris Ltd)

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Syrris Ltd bernoulli naive bayes
Bernoulli Naive Bayes, supplied by Syrris Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/bernoulli+naive+bayes/bernoulli+naive+bayes/10__1016_slash_j__jisa__2021__102794-256-204-189
Average 90 stars, based on 1 article reviews

bernoulli naive bayes - by Bioz Stars, 2026-10

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Article Title: On machine learning effectiveness for malware detection in Android OS using static analysis data
Article Snippet: .. Model Class Threshold Precision Recall F1 OA BA Kappa BNB goodware 0.1 0.997 0.952 0.974 0.952 0.944 0.603 malware 0.469 0.937 0.625 goodware 0.5 0.997 0.956 0.976 0.955 0.946 0.622 malware 0.490 0.936 0.463 goodware 0.1 0.999 0.981 0.990 0.981 0.983 0.805 RF malware 0.695 0.985 0.815 goodware 0.5 0.995 0.999 0.997 0.994 0.945 0.928 malware 0.974 0.891 0.931 goodware -0.3 0.999 0.998 0.998 0.997 0.984 0.965 RidgeReg malware 0.962 0.971 0.966 goodware 0.0 0.998 0.999 0.999 0.998 0.980 0.970 malware 0.980 0.962 0.971 goodware -0.5 0.996 0.996 0.996 0.992 0.955 0.908 LassReg malware 0.909 0.914 0.912 goodware 0.0 0.993 0.999 0.996 0.992 0.920 0.895 malware 0.967 0.841 0.899 goodware -0.6 0.994 0.942 0.967 0.939 0.904 0.523 ANN Malware 0.404 0.866 0.551 goodware -0.4 0.985 0.981 0.983 0.968 0.830 0.627 malware 0.613 0.679 0.644 goodware -0.6 0.994 0.963 0.978 0.959 0.920 0.629 1-ANN malware 0.515 0.877 0.649 goodware -0.4 0.990 0.986 0.988 0.977 0.880 0.730 malware 0.712 0.774 0.742 goodware 0.1 0.999 0.998 0.998 0.997 0.985 0.964 SVM malware 0.959 0.972 0.966 goodware 0.5 0.998 0.999 0.999 0.997 0.979 0.967 malware 0.978 0.959 0.969 5 https://jeodpp.jrc.ec.europa.eu/home/ 6 https://jupyter.org/ V. Syrris and D. Geneiatakis Journal of Information Security and Applications 59 (2021) 102794 Fig. 5. (5a) Bernoulli Naive Bayes: the 40 most important features based on the log probability criterion (the green bars refer to malware and the red bars refer to goodware); (5b) Random Forest: the 40 most important features based on the decrease of the impurity measure when a feature is chosen to split a node (the orange bars represent the feature importance with respect to the entire forest, whereas the black lines show the inter-trees variability); (5c, 5e, 5g, 5i, 5k) additive type models: the 40 most important features based on the highest coefficient value (the green bars refer to positive coefficients and the red bars refer to negative coefficients); (5d, 5f, 5h, 5j, 5l) additive type models: the 40 most important features based on the highest coefficient value multiplied by the standard deviation of the feature (the green bars refer to positive values and the red bars refer to negative values). .. V. Syrris and D. Geneiatakis Journal of Information Security and Applications 59 (2021) 102794 is a high-throughput computing facility at which the processing workflows are executed in a distributed way as instances of custom-made Docker7 images.

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