a novel sensitivity based method for feature selection

https://doi.org/10.1007/s10115-012-0487-8. Segmentation dataset [47]: Features(1) region-centroid-col (2) region-centroid-row (3) short-line-density (4) the results of a line extraction algorithm that counts how many lines of length (5) vedge-mean (6) vedge-sd (7) hedge-mean (8) hedge-sd (9) intensity-mean (10) rawred-mean (11) rawblue-mean (12) rawgreen-mean (13) exred-mean (14) exblue-mean (15) exgreen-mean (16) value-mean (17) saturatoin-mean (18) hue-mean; Target variableClass label 1 (Window), Class label 2 (foilage), Class label 3 (brickface), Class label 4 (path), Class label 5 (cement), Class label 6 (grass), Class label 7 (sky). When the dimension was reduced to 2, the model not only has effective prediction performance, Abstract Background Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. ), (8) Shucked weight (gms. aggregationType Text external He, and J. L. Contreras-Vidal, Deep learning for electroencephalogram (EEG) classification tasks: a review, J. Neural Eng., vol. search boxes above and select the search button. Results In the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we. http://www.aiim.org/pdfa/ns/id/ Implementing an automatic seizure detection model in real time is not trivial. Copyright The feature with a higher magnitude of the first-order derivative is assigned a higher rank and vice versa. CrossmarkDomainExclusive The recognition rate of a single method is not high. 1997;97(12):24571. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. A simple example illustrating the accuracy of CSPA over finite difference schemes can be found elsewhere [38, 39]. URI Privacy For example, Cindy et al. The system detects seizure onsets with an average latency of 15 seconds. Adobe Document Info PDF eXtension Schema statement and The common identifier for all versions and renditions of a document. Regularization methods such as Ridge Regression [ 2 ], Nonnegative Garrote [ 6 ], Least Absolute Selection and Shrinkage Operator (LASSO, [ 8 ]) are the most common forms of embedded methods. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. https://doi.org/10.1016/j.compstruc.2014.04.009. The complex-step derivative approximation. While the proposed method was found to yield lower MSE with only seven top-most features, the mutual information method yielded lower MSE for eleven features for the bodyfat dataset. Have feedback or suggestions for a way to improve these results? [25] developed a technique that analyzes the weights in MLP to determine essential features. XMP Paged-Text Cortez P, Cerdeira A, Almeida F, Matos T, Reis J. 3b, it can be inferred that the trend of ReliefF and the proposed method are similar. CrossmarkDomainExclusive One of the main reasons for choosing these datasets is that they are commonly adopted in the literature of feature selection. \right)\) is the first-order derivative approximation of \(g\left( . Hence the influence of a number of instances on the determination of the important features would also be studied. In other words, ReliefF was found to be effective among all the filter-based methods. CrossMarkDomains Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Eighty percent of the data is used for training the said network after 10-fold cross-validation and the performance of the network is tested with the remaining 20% of the data. The addition of more hidden layers or neurons in each hidden layer to the chosen configuration was found to yield similar MSE errors or accuracies and hence are not considered in this study. The trend of the accuracy for the segmentation dataset is determined for all feature ranking methods with the inclusion of each feature in succession and is shown in Fig. 10.1186/s40537-021-00515-w URI In hybrid methods, multiple conjunct primary feature selection methods are applied consecutively [6]. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. While feature 12 (scaled variance minor), feature 7 (scatter ratio) and feature 8 (elongatedness) was found to be the top three features for symmetric uncertainty, information gain, gain ratio, reliefF and, chi-square, feature 10 (maximum length rectangularity), feature 8 (elongatedness) and feature 5 (axis aspect ratio) was found to be the top 3 features for the proposed method, i.e., feature 8 (elongatedness) was found to be common among top 3 features predicted by all feature ranking methods. https://doi.org/10.1007/978-3-540-77226-2_19. Usual same as prism:doi Classifying biological data into distinct groups is the first step in understanding them. The top 6 features are identified as follows: (5) axis aspect ratio, (8) elongatedness, (10) maximum length rectangularity, (14) skewness major, (17) kurtosis minor and (18) hollow ratio. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Ravi kiran For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The rank of each input feature is then determined based on the magnitude of the first-order derivatives for each perturbed feature \(x_{k}\) determined as shown in Eq. 2018;4:10518. Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. Such errors arising due to the choice of smaller step sizes are referred to as subtractive cancellation errors. Results. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis -Golgi proteins from trans -Golgi proteins. Sensitivity analysis examines the change in the target output when one of the input features is perturbed, i.e., first-order derivatives of the target variable with respect to the input feature are evaluated. Methods. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. https://doi.org/10.1109/TNSRE.2003.814441. Issue number The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. CrossmarkMajorVersionDate To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Gives the name of an author. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. SIAM Rev. Eng Fract Mech. Notwithstanding to methods mentioned above, sensitivity analysis of MLP and support vector machines (SVM) was also carried out to perform feature selection. prism The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the Signal File and Visualizer blocks. currently selected. https://doi.org/10.1016/J.ESWA.2005.09.013. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. Feature selection is a process of identifying a subset of features that dictate the prediction accuracy of the target variables/class labels in a given machine learning task [1,2,3]. Early investigations showed that the prevalence of OP in people > 50 years was 20.7% for women and 14.4% for men in China. The analysis of SNPs helps to identify genetic variants related to complex traits. 4; 2007. the URL). A novel framework is proposed which utilizes different feature selection methods from filters, wrappers, and embedded algorithms. An efficient energy distribution is required as smart devices are increasing dramatically. According to the proposed method, following features are found to be least important as they do not contribute further for reduction of MSE: (5) Chest (cm), (7) Hip (cm), (9) Knee (cm), (10) Ankle (cm), (11) Biceps (cm), (12) Forearm (cm). Zhu J-J, et al. Postmenopausal osteoporosis (PMOP) poses a great health threat to older women. A Design of a Physiological Parameters Monitoring System, Implementing IoT Communication Protocols by Using Embedded Systems. internal Good prediction can help to develop marketing strategies more accurately and to spend resources more effectively. https://doi.org/10.1016/j.dss.2009.05.016. \right)\) with respect to the input \(x_{k}\). EVoR = Enhanced Version of Record Explore Scholarly Publications and Datasets in the NSF-PAR, A novel sensitivity-based method for feature selection, Novel sensitivity method for evaluating the first derivative of the feed-forward neural network outputs, SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. https://doi.org/10.1137/0704019. Furthermore, classification is then performed on selected features to classify the data using a support vector machine (SVM) classifier. Interestingly, in the wine quality dataset, all four feature ranking methods yielded different ranks for the features (see Table 3). Hashem S. Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions, Institute of Electrical and Electronics Engineers (IEEE); 2003; pp. 2. sn internal orcid The purpose of this paper is to develop such a system by using a hybrid approach. 2003;29:24562. where, Imag (*) denotes the imaginary component and \({\mathcal{O}}\left( {h^{2} } \right)\) is the second-order truncation error. A novel sensitivitybased method for feature selection Dayakar L. Naik and Ravi kiran* Introduction Feature selection is a process of identifying a subset of features that dictate the predic-tion accuracy of the target variables/class labels in a given machine learning task [1-3]. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. The remaining novel sensitive prediction results and literature evidences shown in Table 2 indicate that our HNMDRP method can accurately uncover novel sensitive associations between cancer cell . Article Energy is the most important resource in the world. internal The trained model was then evaluated with the online modules. All acquired images have been pre-processed with Simple Median Filter (SMF) and Gaussian Filter (GF) with kernel size (5, 5). The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than themoreexisting studies. Threshold benchmarking for feature ranking techniques, A feature generation algorithm for sequences with application to splice-site prediction, Sentiment Analysis of Tweets Using Machine Learning, 2019, Turkey, Van, pages 85-87, A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem, Scaled Entropy and DF-SE: Different and Improved Unsupervised Feature Selection Techniques for Text Clustering, PREDICTING PROTEIN SECONDARY STRUCTURE BASED ON ENSEMBLE NEURAL NETWORK, Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques, Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets, Attribute selection methods for filtered attribute subspace based bagging with injected randomness (FASBIR), A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis, Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem, Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis, Artificial neural networks in Bayesian inference, EEG Signal Analysis for BCI Application using Fuzzy System, UNSUPERVISED APPROACHES FOR THE GENERATION OF STRUCTURES ON LARGE DATA, Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments, Efficient Dissemination of Rainfall Forecasting to Safeguard Farmers from Crop Failure Using Optimized Neural Network Model, A Novel Approach Towards Online Devnagari Handwritten Word Recognition Based on Robust Feature Extraction Method and FFNN Classifier, Feature ranking methods based on information entropy with Parzen windows. Comparison of the complex-step sensitivity method with other feature selection methods for the classification task. 5. Note that often complete dataset may not be required for training the FFNN when the size of the dataset is large. The higher the magnitude of change in feature sensitivity metric, the higher is the importance of input feature. MATH name MathSciNet Google Scholar. The proposed method yielded an accuracy of 75% by selecting only the top 6 features and was found to outperform the other feature ranking methods. While symmetric uncertainty, information gain, gain ratio, reliefF and, chi-square identified feature 10 (fractal dimension1), feature 12 (texture2), and feature 15 (smoothness2) as least relevant, the proposed method identified the feature 3 (perimeter1), feature 5 (smoothness1) and feature 27 (concavity3) are least relevant. Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. 356362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. The date when a publication was publishe. 2022 BioMed Central Ltd unless otherwise stated. In other words, the performance of FFNN for the only top-most feature is first assessed, and then the process is repeated by including the second most important feature and so on. A structure containing the characteristics of a font used in a document. Since customer identification is one of the principal concerns in the insurance industry, an insurance company dataset has been used. The details of the dataset are provided in section Numerical experiments and the efficacy of the proposed method is then demonstrated on real-world datasets in section Results, and the summary and future work are provided in Section Summary and future work. Followed by the determination of FFNN configuration, the rank of the features in each dataset is evaluated using the proposed method. These first-order derivatives will aid in providing information about the importance of the input features. 2019;112: 103375. https://doi.org/10.1016/j.compbiomed.2019.103375. 1996. https://doi.org/10.1080/10691898.1996.11910505. where, \(r = 1 \ldots ..m\) and \(m\) indicates the number of class labels. The attribute platform is optionally allowed for situations in which multiple URLs must be specified. external Cite this article. Note that in the case of the classification task, the partition ratio is maintained consistently for each class label, i.e., 70:15:15 of training, validation, and testing data from each class label is chosen. The final conference submission will include a more detailed analysis of the online performance of each module. Text Text https://doi.org/10.1109/cifer.1995.495263. Furthermore, the trend of the accuracy is determined for vehicle dataset for all feature ranking methods with the inclusion of each feature in succession and is shown in Fig. Department of Civil & Environmental Engineering, North Dakota State University, Fargo, ND, 58105, USA, You can also search for this author in internal 1 0 obj http://www.niso.org/schemas/jav/1.0/ internal [23] presented a maximum output information algorithm for feature selection. 2021-09-30T16:07:17+05:30 CorrAUC 43,44 is a correlation based wrapper feature selection method developed to detect the . Evaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. 2003;12:11925. CSPA, originally referred to as complex-step derivative approximation (CSDA), was proposed by Lyness and Moler [36] to evaluate the first-order derivative of analytic functions. 3c, reveals that all feature ranking methods performed more or less similar. Breast cancer diagnosis and prognosis via linear programming. In sensitivity analysis, each input feature is perturbed one-at-a-time and the response of the machine learning model is examined to determine the feature's rank. Oper Res. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. The system then displays the EEG signal and the decisions simultaneously using a visualization module. In future work, the authors intend to extend the proposed method to the multiple output regression problems. The Author(s) It evaluates the analytical quality first-order derivatives without the need for extra computations in neural networks or SVM machine learning models. 2004;15:93748. SA methods is predominantly classified into two types: qualitative and quantitative methods [10], as shown in Fig. By taking the imaginary component of \(f\left( {x_{0} + ih} \right)\), and truncating the higher-order terms in the Taylor series, the first-order derivative can be expressed as. IEEE Trans Neural Networks. In the proposed method, we implement a complex-step perturbation in the framework of feed-forward neural networks to illustrate the task of feature selection. The descriptive features and target variables for each dataset are mentioned as follows. In the first stream, the feature extractor receives the signals using stdin. By using our site, you agree to our collection of information through the use of cookies. 233274. a blank value for editor search in the parent form. (auto-classified) A novel feature selection method based on global sensitivity analysis with application in machine learning-based prediction model Computing methodologies Modeling and simulation Model development and analysis Comments 39 View Issue's Table of Contents back Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer. seq Text Comput Biol Med. [35] introduced the iterative perturbation method for auto-tuning the step size for SVM. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. Crossmark Schema UCI Machine Learning Repository 2021. https://archive.ics.uci.edu/ml/index.php. Other supervised ML classification algorithms will be employed, and the efficacy of the proposed method will be examined. [4] CFM Olympic Brainz Monitor. [Online]. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients, Rev. SIAM; 2017. These methods could be broadly grouped into six categories, namely, filter methods, wrapper methods, embedded methods, hybrid methods, ensemble methods, and integrative methods [5,6,7]. Integer Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. Text The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified. external A name object indicating whether the document has been modified to include trapping information internal true While the proposed method was found to outperform other popular feature ranking methods for classification datasets (vehicle, segmentation, and breast cancer), it was found to perform more or less similar with other methods in the case of regression datasets (body fat, abalone, and wine quality). xmpMM

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