An approach is provided for training classifiers used in machine learning. A corpus of training data is received. One or more clusters of the training data is generated according to features of the training data. The one or more clusters are refined using user-specified rules. One or more ...
In this study, we investigate how an organism’s codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build several AI/machine learning models. When trained on...
Saeh, I. S., Mustafa, M. W., Machine Learning Classifiers for Steady State Security Evaluation in Power System. International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012.I. Saeh and M.W.Mustafa, "Machine learning classifiers for steady state security evaluation...
M, "Performance Evaluation of Machine Learning Classifiers in Sentiment Mining," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 6, 2013.Vinodhini G, Chandrasekaran R. Performance evalua- tion of machine learning classifiers in sentiment mining. International Journal of...
The first seven numeric values on each line are the predictor values, often called attributes or features in machine learning terminology. The predictors are seed area, perimeter, compactness, length, width, asymmetry coefficient, and groove length. The item-to-predict (often ...
Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different prediction. We posit that e...
needing any expertise in machine learning. For instance, a custom classifier can be built to classify loan contracts, invoices, and project documents. Together, both built-in and build-your-own trainable classifiers provide classification support for a breadth of catego...
Benchmark datasets are the inevitable tool required to scrutinize vulnerabilities and tools in network security. Current datasets lack correlation between normal and the real-time network traffic. Behind every evaluation and establishment of attack detec
Explore related subjects Discover the latest articles and news from researchers in related subjects, suggested using machine learning. Bayesian Network Bayesian Inference Statistical Learning Machine Learning Learning algorithms Probabilistic data networks ...
The proposed method is not only suitable for safety evaluation of machine learning classifiers but also can be used @Run-Time as an eXplainable AI (XAI). In one of ourexamples for security dataset, we showed how SafeML can be used as XAI. ...