Novel Frequency Domain Classification Algorithm Based On Parameter Weight Factor Computation
EL-Bohy, A., Hashad, A., Taha, H., Azouz, A. (2018). Novel Frequency Domain Classification Algorithm Based On Parameter Weight Factor Computation. EKB Journal Management System, 11(11th International Conference on Electrical Engineering ICEENG 2018), 1-12. doi: 10.21608/iceeng.2018.30148
Ahmed M. Salah EL-Bohy; Atallah I. Hashad; Hussien Saad Taha; Ahmed Azouz. "Novel Frequency Domain Classification Algorithm Based On Parameter Weight Factor Computation". EKB Journal Management System, 11, 11th International Conference on Electrical Engineering ICEENG 2018, 2018, 1-12. doi: 10.21608/iceeng.2018.30148
EL-Bohy, A., Hashad, A., Taha, H., Azouz, A. (2018). 'Novel Frequency Domain Classification Algorithm Based On Parameter Weight Factor Computation', EKB Journal Management System, 11(11th International Conference on Electrical Engineering ICEENG 2018), pp. 1-12. doi: 10.21608/iceeng.2018.30148
EL-Bohy, A., Hashad, A., Taha, H., Azouz, A. Novel Frequency Domain Classification Algorithm Based On Parameter Weight Factor Computation. EKB Journal Management System, 2018; 11(11th International Conference on Electrical Engineering ICEENG 2018): 1-12. doi: 10.21608/iceeng.2018.30148

Novel Frequency Domain Classification Algorithm Based On Parameter Weight Factor Computation

The International Conference on Electrical Engineering
Article 23, Volume 11, 11th International Conference on Electrical Engineering ICEENG 2018, April 2018, Page 1-12  XML PDF (524.89 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2018.30148
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Authors
Ahmed M. Salah EL-Bohy1; Atallah I. Hashad1; Hussien Saad Taha2; Ahmed Azouz2
1Arab Academy for Science, Technology & Maritime Transport, Cairo, Egypt.
2Egyptian Armed Forces.
Abstract
This paper considered a state of art as it employs a brand new technique to estimate the missing values in the dataset helping the classifiers to classify the data with better accuracy. also determining the effect of each attribute on the accuracy that enables researchers to get better or same accuracy with less number of attributes saving processing time, RAM, and memory needed. The aim of this paper is to improve accuracy of expecting Hepatitis mortality using worldwide dataset from Ljubljana University. We present an implementation of two brand new classification techniques. Using confusion matrix and K-fold cross validation technique to calculate classification accuracy. Two experiments have been done and the experimental results show that using the correlation in frequency domain after computing the weight factor for each attribute achieved better accuracy than using the subtraction method in time domain.
Keywords
Hepatitis; K-fold cross validation; classification; Classification in frequency domain Confusion Matrix
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