One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrom...One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.展开更多
Aiming to deficiency of the filter and wrapper feature selection methods, anew method based on composite method of filter and wrapper method is proposed. First the methodfilters original features to form a feature sub...Aiming to deficiency of the filter and wrapper feature selection methods, anew method based on composite method of filter and wrapper method is proposed. First the methodfilters original features to form a feature subset which can meet classification correctness rate,then applies wrapper feature selection method select optimal feature subset. A successful techniquefor solving optimization problems is given by genetic algorithm (GA). GA is applied to the problemof optimal feature selection. The composite method saves computing time several times of the wrappermethod with holding the classification accuracy in data simulation and experiment on bearing faultfeature selection. So this method possesses excellent optimization property, can save more selectiontime, and has the characteristics of high accuracy and high efficiency.展开更多
This paper proposes a hybrid feature selection sequence comple-mented with filter and wrapper concepts to improve the accuracy of Machine Learning(ML)based supervised classifiers for classifying the survivability of b...This paper proposes a hybrid feature selection sequence comple-mented with filter and wrapper concepts to improve the accuracy of Machine Learning(ML)based supervised classifiers for classifying the survivability of breast cancer patients into classes,living and deceased using METABRIC and Surveillance,Epidemiology and End Results(SEER)datasets.The ML-based classifiers used in the analysis are:Multiple Logistic Regression,K-Nearest Neighbors,Decision Tree,Random Forest,Support Vector Machine and Multilayer Perceptron.The workflow of the proposed ML algorithm sequence comprises the following stages:data cleaning,data balancing,feature selection via a filter and wrapper sequence,cross validation-based training,testing and performance evaluation.The results obtained are compared in terms of the following classification metrics:Accuracy,Precision,F1 score,True Positive Rate,True Negative Rate,False Positive Rate,False Negative Rate,Area under the Receiver Operating Characteristics curve,Area under the Precision-Recall curve and Mathews Correlation Coefficient.The comparison shows that the proposed feature selection sequence produces better results from all supervised classifiers than all other feature selection sequences considered in the analysis.展开更多
With the arrival of big-data society, methods for classifying real-world problems have attracted much attention for researchers and developers in various fields. In recent years, much effort has been devoted for impro...With the arrival of big-data society, methods for classifying real-world problems have attracted much attention for researchers and developers in various fields. In recent years, much effort has been devoted for improving performances of classification algorithms by adding functions or modifying their weaknesses. However, since a large variety of classification algorithms has been available, it is difficult for non-experts to find classification algorithms that achieve good results on a given data set. Therefore, if there is a system which automatically selects the best classification algorithm for a given data set, non-experts would receive various benefits such as saving time and effort. This paper presents a system of predicting the best possible classification algorithm for a given data set with respect to the accuracy. To the best of our knowledge, this is the first approach focused on predicting the best one. The main target users of the proposed system are non-experts who do not have knowledge and experience in data mining. The proposed system utilizes useful meta-features selected from existing recta-features to increase the performance of the prediction. The feature selection is conducted by a wrapper approach with the genetic search algorithm. In the proposed system, K-nearest neighbor algorithm is used to learn the selectedmeta-features and build a classification model for predicting future data. Experiments using 58 real-world data sets show that the proposed system predicted the best classification algorithm with 60.34% accuracy from the top five in 30 classification algorithms.展开更多
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number WE-44-0033.
摘要One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.
基金This project is supported by Scientific Research Foundation of National Defence of China (No.41319040202).
摘要Aiming to deficiency of the filter and wrapper feature selection methods, anew method based on composite method of filter and wrapper method is proposed. First the methodfilters original features to form a feature subset which can meet classification correctness rate,then applies wrapper feature selection method select optimal feature subset. A successful techniquefor solving optimization problems is given by genetic algorithm (GA). GA is applied to the problemof optimal feature selection. The composite method saves computing time several times of the wrappermethod with holding the classification accuracy in data simulation and experiment on bearing faultfeature selection. So this method possesses excellent optimization property, can save more selectiontime, and has the characteristics of high accuracy and high efficiency.
摘要This paper proposes a hybrid feature selection sequence comple-mented with filter and wrapper concepts to improve the accuracy of Machine Learning(ML)based supervised classifiers for classifying the survivability of breast cancer patients into classes,living and deceased using METABRIC and Surveillance,Epidemiology and End Results(SEER)datasets.The ML-based classifiers used in the analysis are:Multiple Logistic Regression,K-Nearest Neighbors,Decision Tree,Random Forest,Support Vector Machine and Multilayer Perceptron.The workflow of the proposed ML algorithm sequence comprises the following stages:data cleaning,data balancing,feature selection via a filter and wrapper sequence,cross validation-based training,testing and performance evaluation.The results obtained are compared in terms of the following classification metrics:Accuracy,Precision,F1 score,True Positive Rate,True Negative Rate,False Positive Rate,False Negative Rate,Area under the Receiver Operating Characteristics curve,Area under the Precision-Recall curve and Mathews Correlation Coefficient.The comparison shows that the proposed feature selection sequence produces better results from all supervised classifiers than all other feature selection sequences considered in the analysis.
摘要With the arrival of big-data society, methods for classifying real-world problems have attracted much attention for researchers and developers in various fields. In recent years, much effort has been devoted for improving performances of classification algorithms by adding functions or modifying their weaknesses. However, since a large variety of classification algorithms has been available, it is difficult for non-experts to find classification algorithms that achieve good results on a given data set. Therefore, if there is a system which automatically selects the best classification algorithm for a given data set, non-experts would receive various benefits such as saving time and effort. This paper presents a system of predicting the best possible classification algorithm for a given data set with respect to the accuracy. To the best of our knowledge, this is the first approach focused on predicting the best one. The main target users of the proposed system are non-experts who do not have knowledge and experience in data mining. The proposed system utilizes useful meta-features selected from existing recta-features to increase the performance of the prediction. The feature selection is conducted by a wrapper approach with the genetic search algorithm. In the proposed system, K-nearest neighbor algorithm is used to learn the selectedmeta-features and build a classification model for predicting future data. Experiments using 58 real-world data sets show that the proposed system predicted the best classification algorithm with 60.34% accuracy from the top five in 30 classification algorithms.