%0 Journal Article %1 noauthororeditor %A El-Badie, S. Abd %D 2022 %E AMS, %J AMS %K Bayes Gamma Naïve %N https://doi.org/10.5121/ams.2022.9402 %P 14 %R https://doi.org/10.5121/ams.2022.9402 %T Submit your research artical!!! %U https://airccse.com/ams/papers/9422ams02.pdf %V 9 %X In this paper, an innovative Statistical Data Mining (SDM) technique is proposed using Gamma Mining Procedure (GMP) contributing a new classifier & predictor by applying very effective stages on the training and testing data depending on Gamma (G) correlation matrix and Gamma absorption process. Linking the previous stages with the Misclassification Error (MError) as a precision measure for obtaining a new classifier and a new predictor, then using the novel predictor for attributes and objects mining of the test data. Applying the last GMP stage by using the contributed predictor attributes with Naive Bayes technique for prediction. The proposed GMP technique is applied and examined on a Breast Cancer Tumor diagnosis to demonstrate its applicability. Two SDM validation tools are used with the new SDM technique, the 1st versus cross validation with bootstrapping using Rapid Miner as a DM tool, and the 2nd versus two step cluster analysis, using SPSS Modeler. %@ https://doi.org/10.5121/ams.2022.9402

@article{noauthororeditor, abstract = {In this paper, an innovative Statistical Data Mining (SDM) technique is proposed using Gamma Mining Procedure (GMP) contributing a new classifier & predictor by applying very effective stages on the training and testing data depending on Gamma (G) correlation matrix and Gamma absorption process. Linking the previous stages with the Misclassification Error (MError) as a precision measure for obtaining a new classifier and a new predictor, then using the novel predictor for attributes and objects mining of the test data. Applying the last GMP stage by using the contributed predictor attributes with Naive Bayes technique for prediction. The proposed GMP technique is applied and examined on a Breast Cancer Tumor diagnosis to demonstrate its applicability. Two SDM validation tools are used with the new SDM technique, the 1st versus cross validation with bootstrapping using Rapid Miner as a DM tool, and the 2nd versus two step cluster analysis, using SPSS Modeler. }, added-at = {2024-08-01T07:28:31.000+0200}, author = {El-Badie, S. Abd}, biburl = {https://www.bibsonomy.org/bibtex/2769369e69000dd34acc78d82192eb77a/abizanmiyar28}, description = {In this paper, an innovative Statistical Data Mining (SDM) technique is proposed using Gamma Mining Procedure (GMP) contributing a new classifier & predictor by applying very effective stages on the training and testing data depending on Gamma (G) correlation matrix and Gamma absorption process. Linking the previous stages with the Misclassification Error (MError) as a precision measure for obtaining a new classifier and a new predictor, then using the novel predictor for attributes and objects mining of the test data. Applying the last GMP stage by using the contributed predictor attributes with Naive Bayes technique for prediction. The proposed GMP technique is applied and examined on a Breast Cancer Tumor diagnosis to demonstrate its applicability. Two SDM validation tools are used with the new SDM technique, the 1st versus cross validation with bootstrapping using Rapid Miner as a DM tool, and the 2nd versus two step cluster analysis, using SPSS Modeler. }, doi = {https://doi.org/10.5121/ams.2022.9402}, editor = {AMS}, identifier = {https://doi.org/10.5121/ams.2022.9402}, interhash = {2d2f8e935af0fbbfa9d4357b0c374048}, intrahash = {769369e69000dd34acc78d82192eb77a}, isbn = {https://doi.org/10.5121/ams.2022.9402}, issn = {https://doi.org/10.5121/ams.2022.9402}, journal = {AMS}, keywords = {Bayes Gamma Naïve}, language = {AMS}, month = {DECEMBER}, note = {https://doi.org/10.5121/ams.2022.9402}, number = {https://doi.org/10.5121/ams.2022.9402}, pages = 14, subjectarea = {ARTIFICIAL INTELLIGENT}, timestamp = {2024-08-01T07:28:31.000+0200}, title = {Submit your research artical!!!}, url = {https://airccse.com/ams/papers/9422ams02.pdf}, volume = 9, year = 2022 }