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  • br Feature selection rate is

    2020-08-18


    Feature selection rate is defined as the ratio of number of relevant features that are correctly selected to the total number of features. It is measured in terms of percentage (%). The mathe-matical formula for feature selection rate is measured as follows,
    number of correctly selected features FSR =
    Total number of
    features
    In (19), ‘FSR’ denotes a Feature Section Rate. The sample cal-culation for feature selection rate using the five methods is given as follows:
    Sample calculations:
    • Proposed WONN-MLB: With ‘20’ features considered for experimentation and the number of features correctly selected is 18, then the feature selection rate is calculated as follows: 18 FSR = ∗ 100 = 90%
    • NSCLC: With ‘20’ features considered for experimentation and the number of features correctly selected is 17, then the
    Fig. 8. Performance measure of feature selection rate.
    • BSVM: With ‘20’ features considered for experimentation and the number of features correctly selected is 16, then the feature selection rate is calculated as follows: • 16 FSR = ∗ 100 = 80%
    • NPPC: With ‘20’ features considered for experimentation and the number of features correctly selected is 14, then the feature selection rate is calculated as follows: 14 FSR = ∗ 100 = 72%
    • MV-CNN: With ‘20’ features considered for experimentation and the number of features correctly selected is 13, then the feature selection rate is calculated as follows: 13 FSR = ∗ 100 = 65%
    Fig. 8 depicts the feature selection rate comparison between proposed approach and existing NSCLC, BSVM, NPPC, and MV-CNN, respectively. In order to conduct the experiments, 20 to 200 features are considered. The performance analysis of feature selection rate using proposed WONN-MLB approach is compared with existing NSCLC, BSVM, NPPC, and MV-CNN. When consider-ing 20 number of features for the performance analysis, the pro-posed WONN-MLB approach provides the feature selection rate of 90%, whereas the existing NSCLC, BSVM, NPPC and MV-CNN obtains 85%, 80%, 72%, and 65%, respectively. From the discussion, it 80306-38-3 is clear that the feature selection rate using proposed WONN-MLB approach is higher as compared to other existing [1–3] and [17] methods. This is due the application of identifying maxi-mum relevancy between set of attributes and reducing minimum redundancy attributes in preprocessing. This helps to selects the accurate features for cancer disease diagnosis. Therefore, feature selection rate is improved using proposed WONN-MLB approach by 10%, 18%, 28%, and 41% as compared to NSCLC by Wu et al. [1], BSVM by Zięba et al. [2], NPPC by Ghorai et al. [3], and MV-CNN by Liu et al. [17], respectively. 
    5. Conclusion
    An effective Weight Optimized Neural Network with Maxi-mum Likelihood Boosting for LCD in big data is investigated to im-prove the LCD diagnosis accuracy and to minimize the false pos-itive rate as well as classification time. To achieve these, the pre-processing the model using Newton–Raphson’s MLMR attributes retrieved and remove the irrelevant features is used. Therefore, the classification time gets minimized. With the most relevant attributes, an ensemble classification model called Weighted Op-timized Neural Network and Boosting is applied for early lung cancer diagnosis with a higher accuracy rate. Here, not only the weighted sum function is considered, but also the most opti-mal values are obtained. The final ensemble technique finds the weak classifier with less error value and new component update based on the error function. This process attains higher disease diagnosing accuracy with the minimum false positive rate. Exper-imental evaluation is conducted with different parameters such as-disease diagnosing accuracy, false positive rate, and classifica-tion. The experimental results show that the proposed approach achieved accurate results for big data processing as compared to existing methods. Proposed WONN-MLB approach is tested with different dataset, but still there is huge amount of data points are presented which need to be tested with the proposed approach in future.
    Declaration of competing interest
    No author associated with virus paper has disclosed any po-tential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.asoc.2019.04.031.
    References
    [1] Jia Wu, Yanlin Tan, Zhigang Chen, Ming Zhao, Decision based on big data research for non-small cell lung cancer in medical artificial system in developing country, Comput. Methods Programs Biomed. 159 (2018) 87– 101, [Big data research in Non-Small Cell Lung Cancer – Big data research in NSCLC].