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They can also be seen as linear classifiers with weights \(s_i\) restricted to be elements of the score set \(\mathcal {S}\). When features are strictly binary, scoring systems essentially resemble Boolean threshold functions and share many of their properties (Crama & Hammer, 2011). ...
A key advantage of the approach is that it allows reaching near zero misclassification error rate when applying standard learning classifiers. In addition, probability based performance evaluation metrics have been proposed as alternatives to the conventional metrics. The usage of those provides the ...
The highest performance of the classifiers reached A U C R O C of 0.70. The analysis of grammar parse trees revealed the ability of representing structural features of helix‐helix contact sites. Conclusions We demonstrated that our probabilistic context‐free framework for analysis of protein ...
Multi-class classifiers Decision trees Naive Bayes Neighbors classifiers and regressors Multi-dimensional interpolators High dimension model reduction (HDMR) Morse-Smale complex Model capabilities Data Post-Processing capabilities Data clustering Data regression ...
We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visi...
Classification-based evaluation of integrins, integrin-binding, and biofilm formation motifs: The performances of machine learning classifiers in phenotype prediction using the extracted motifs as features are provided in Table 2 evaluated in both 10-fold cross-validation scheme, as well as in classifyin...
in the ensemble (Fred2001; Fred and Jain2002; Strehl and Ghosh2003). Leveraging an ensemble of clusterings is considerably more difficult than combining an ensemble of classifiers, due to the label correspondence problem: how to put in correspondence the cluster labels produced by different ...
technique of this sort is a Naïve Bayes classifier. Naïve Bayes classifiers have found use in certain email systems to help in rejecting unwanted, or “spam,” messages as they arrive over a network at an email server, for example, but not to existing files stored in a computer system...