Pham, H.N.A., Triantaphyllou, E.: The impact of overfitting and overgeneralization on the classification accuracy in data mining. In: Maimon, O., Rokach, L. (eds.) Soft Computing for Knowledge Discovery and Data
In subject area:Computer Science Classification accuracy refers to the rate of correct classifications achieved when comparing different methods on a data set that is naturally grouped into subsets. It is measured by determining the accuracy of classifying new observations based on a rule developed usin...
The fitness of a rule is assessed by its classification accuracy on a set of training samples. The genetic operators such as crossover and mutation are applied to create offspring. In crossover, the substring from pair of rules are swapped to form a new pair of rules. In mutation, randomly...
The accuracy is defined by the fraction of correctly classified data points: (22.22)accuracy=TP+TNTP+TN+FP+FN. Considering our example of classifying WSIs, we can easily see that accuracy will fail to assess the performance of a model that is always predicting 0. In this case the model ...
The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy....
A Hybrid Data Mining Technique for Improving the Classification Accuracy of Microarray Data Set. Information Engineering and Electronic Business. 2012; 2:43-50.S. Dash, B. Patra, and B. K. Tripathy, "A hybrid data mining tech- nique for improving the classification accuracy of microarray data...
By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by ...
Most comparisons between methods are based only on total classification accuracy and/or error rates; they involve human intervention for training and optimization of the data mining classifiers vs. out-of-the-box results for the traditional classifiers. Furthermore, in medical contexts, sensitivity (...
Most comparisons between methods are based only on total classification accuracy and/or error rates; they involve human intervention for training and optimization of the data mining classifiers vs. out-of-the-box results for the traditional classifiers. Furthermore, in medical contexts, sensitivity (...
In this study SMOTE is the key concept which we used to improve the accuracy. For balancing the dataset, number of instances may be under sampled. This can be applied in the preprocessing stage of data mining. 3. Methodology This section discusses the dataset description and suggested ...