Feature Selection Tree Research Articles

DCM-GIFT: An Android malware dynamic classification method based on gray-scale image and feature-selection tree

Context:The boom of Android market makes mobile products more popular and convenient. However, in the face of the complex Android application market, how to efficiently and accurately identify malware has become one of the focuses of research. Various new types of disguised malware lurk in the web pages, links and major application malls. Therefore, people’s privacy and property security have become a major obstacle to the continued development of mobile devices. Objective:Most of the existing malware classification methods are fixed on one or several types of characteristics of Android devices, such as static characteristics, dynamic characteristics and traffic characteristics. Single feature detection or fixed feature fusion models limit the dimension of detection software, and also cause imbalanced classification results. This paper proposes an Android Malware Dynamic Classification Method based on Gray-scale Image and Feature-selection Tree (DCM-GIFT), which aims to improve and stabilize the precision of Android software classification and enhance the robustness of malware classification. Method:In this paper, we construct gray-scale images for the original Android traffic to retain the characteristics of the time series and spatial structure of the original network traffic. At the same time, we take the dynamic information and static information of Android software as auxiliary features to build a feature selection tree. The feature-selection algorithm helps the classifier dynamically select the optimal feature fusion scheme, and the resulting fusion feature vector will be trained and predicted using machine learning clusters for model training. Results:We evaluate the performance of DCM-GIFT on multiple datasets published at the Canadian Institute for Cybersecurity, the area under the accuracy, precision, recall and F1measure. The results show that the proposed DCM-GIFT model has significantly better prediction performance compared to other software classification models. Conclusion:It can be concluded that: (1) In terms of accuracy, precision, recall and F1measure, the DCM-GIFT model has a higher average value. (2) The DCM-GIFT model effectively solves the problem of imbalanced classification results in Android software. (3) The DCM-GIFT model achieves the goal of dynamic feature fusion and significantly improves the utilization of system resources.

OPTIMIZED RADIOMICS-BASED MACHINE LEARNING APPROACH FOR LUNG CANCER SUBTYPE CLASSIFICATION

Lung cancer is a major global health concern and a leading cause of cancer-related deaths. Accurate diagnosis and treatment of lung cancer are crucial for improving patient outcomes. Subtyping lung cancer provides essential information about its molecular characteristics, clinical behavior, and prognosis, thereby guiding treatment planning. Radiomics, a novel discipline, offers a promising approach to characterize the tumor microenvironment by extracting quantitative imaging features from medical images. Radiomics aims to comprehensively and non- invasively characterize tumors and their microenvironment, enabling the identification of tumor subtypes, prediction of therapy response, and enhancement of patient outcomes. This study evaluates the effectiveness of a Particle Swarm Optimization-Random Forest (PSO-RF) classifier for subtype categorization of lung cancer based on radiomics using computed tomography (CT) images. The study utilizes three datasets, extracting 1093 radiomic features and reducing them to 20 significant features through extra tree feature selection. Optimized parameters of the PSO-RF classifier are determined using 10-fold cross-validation and compared to traditional machine learning classifiers and reported works. Results demonstrate that the PSO-RF classifier outperforms other methods, achieving an accuracy of 92%, precision of 92.5%, recall of 92%, and [Formula: see text] 1-score of 0.92 in the Lung1 dataset. Training on Dataset 3 and validating the Lung3 dataset confirm the generalizability of the model, yielding an accuracy of 87% and an AUC of 0.91 across diverse scenarios. These findings highlight the efficacy of radiomics in identifying lung cancer subtypes and demonstrate the potential of the PSO-RF classifier. The incorporation of radiomics into clinical practice has the potential to greatly improve patient outcomes by customizing treatment approaches according to unique tumor characteristics. The demonstrated effectiveness of the PSO-RF classifier makes it a valuable resource for diagnosing and categorizing different subtypes of lung cancer.

A novel global solar exposure forecasting model based on air temperature: Designing a new multi-processing ensemble deep learning paradigm

The total quantity of solar energy falling on a horizontal plane surface is the global solar exposure (GSE, i.e., total solar energy). Precise forecasting of GSE is important in many fields such as renewable energy, agriculture, and public health, particularly by the limited hydro-meteorological time series information. This research aims to develop an advanced multi-processing deep learning (DL) paradigm to forecast weekly GSE based on maximum (Tmax) and minimum (Tmin) air temperatures as the drivers at Brisbane and Perth airport stations in eastern and western Australia during 2000 to 2022. The proposed model was comprised of an extra tree feature selection (FS) integrated with two novel decomposition techniques, namely time-varying filtering-based empirical mode decomposition (TVF-EMD), and empirical wavelet transform (EWT), and a powerful ensemble deep random vector functional link (ED-RVFL) approach. To validate the main model, the RVFL, bidirectional long-short term memory (Bi-LSTM), and bagged regression tree (Bagging) machine learning (ML) models were examined in hybrid and standalone counterpart frameworks. First, the extra-tree FS determined the significant lags of the predictors based on an importance benchmark criterion. Then, by applying the optimal gained lags to the feeding models, all of the original predictors were decomposed using TVF-EMD and EWT univariate feature extraction. The final forecast was computed by aggregating all the individual forecasts of the intrinsic mode functions (IMFs) and residual components. In addition, eight statistical indicators (including coefficient of correlation: R, root mean square error: RMSE, Kling-Gupta efficiency: KGE, index of agreement: IA, uncertainty coefficient with 95% confidence level: U95%, mean absolute percent error: MAPE, Nash-Sutcliffe efficiency: NSE, and mean absolute error: MAE) and several graphical methods were utilized to evaluate the performances of the models. The modeling results indicated that the ED-RVFL-TVF-EMD (R = 0.9665, RMSE = 1.9193 MJ/m2, and KGE = 0.9565 for the Perth airport station) and ED-RVFL-EWT (R = 0.9218, RMSE = 1.9708 MJ/m2, and KGE = 0.8552 for the Brisbane station) outperformed all other models, followed by RVFL, Bi-LSTM, and Bagging in a hybrid format. With the high predictive robustness, both decomposition-based frameworks can be useful for solving energy forecasting problems. The new modeling approach developed in this study can provide more precise forecasts for decision-makers to better address climate change, agriculture, and energy crises.

Discovery of Approaches by Various Machine learning Ensemble Model and Features Selection Method in Critical Heart Disease Diagnosis

Heart disease is one of the leading killers that are widely recognized through- out the globe. Large volumes of clinical data are stored in a variety of sys- tems and biological equipment at hospitals. It is essential to grasp the facts of heart disease in order to improve forecast accuracy. In this paper, experimental evaluations have been conducted to assess the effectiveness of models created utilizing classification algorithms and relevant attributes selected using Extra Tree feature selection procedures. Several people suffer originated at heart disease globally. It is necessary to use data mining and machine learning techniques to extract new insights originated at this data. Analyzing medical data sets and diagnostic issues, including heart disease, involved the use of a number of categorization approaches. However, these methods were only per- formed on small, balanced data; then, the features must be derived originated at trial and error. Additionally, several sectors have made substantial use of feature selection techniques to enhance classification performance. This paper aims to propose a comprehensive approach to enhance the prediction of heart disease using several machine learning methods such as Bagging, Support Vec- tor Machine, Multilayer Perception and Gradient Boost with feature selection methods such as extra tree. The experimental results showed improvements of prediction. Bagging received scores in training model on 80% data sample as 99.08, 73.19, 67.20, 69.20 and 80.66 of accuracy, precision, recall, F1-score and roc respectively. In the experiment, we have tested on 20% data sam- ple for each classifier algorithms and find Bagging classifier model perform higher score for accuracy, precision, recall, F1-score and roc 92.62, 48.44, 39.63, 41.89, 66.82 respectively.

Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study.

Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient. In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. While reducing the feature set search space, this method can balance model simplicity and learning performance to select the best feature subset. We leverage the interpretability of gradient boosting trees to aid in understanding key features of ACS, linking the eigenvalue meaning of instances to model risk predictions to provide interpretability for the classifier. The data set used in this paper is patient records after percutaneous coronary intervention (PCI) in a tertiary hospital in Fujian Province, China from 2016 to 2021. In this paper, we experimentally explored the impact of our method on ACS risk prediction. We extracted 25 key variables from 430 complex ACS medical features, with a feature reduction rate of 94.19%, and identified 5 key ACS factors. Compared with different baseline methods (Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Networks), the results show that our method achieves the highest Accuracy of 98.8%.

A Fused Multidimensional EEG Classification Method Based on an Extreme Tree Feature Selection.

When a brain-computer interface (BCI) is designed, high classification accuracy is difficult to obtain for motor imagery (MI) electroencephalogram (EEG) signals in view of their relatively low signal-to-noise ratio. In this paper, a fused multidimensional classification method based on extreme tree feature selection (FMCM-ETFS) is proposed for discerning motor imagery EEG tasks. First, the EEG signal was filtered by a Butterworth filter for preprocessing. Second, C3, C4, and CZ channels were selected to extract time-frequency domain and spatial domain features using autoregressive (AR), common spatial pattern (CSP), and discrete wavelet transform (DWT). The extracted features were fused for a further feature elimination. Then, the features were selected using three feature selection methods: recursive feature elimination (RFE), principal component analysis method (PCA), and extreme trees (ET). The selected feature vectors were classified using support vector machines (SVM). Finally, a total of twelve subjects' EEG data from Inner Mongolia University of Technology (IMUT data), the 2nd BCI competition in 2003, and the 4th BCI competition in 2008 were employed to show the effectiveness of this proposed FMCM-ETFS method. The results show that the classification accuracy using the multidimensional fused feature extraction (AR + CSP + DWT) is 3%–20% higher than those using the aforementioned three single feature extractions (AR, CSP, and DWT). Extreme trees (ET), which is a sort of tree-based model method, outperforms RFE and PCA by 1%–9% in term of classification accuracies, when these three methods were applied to the procedure of feature extraction, respectively.

Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

The classification of movement intention through machine learning models: the identification of significant time-domain EMG features.

Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject’s intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects’ biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.

Coronary artery disease diagnosis using extra tree-support vector machine: ET-SVMRBF

Coronary Artery Disease (CAD) is a type of cardiovascular disease that can lead to cardiac arrest if not diagnosed timely. Angiography is a standard method adopted to diagnose CAD. This method is an invasive method having certain side effects. So there is a need for non-invasive methods to diagnose CAD using clinical data. In this paper, authors have proposed a methodology ET-SVMRBF (Extra Tree SVM-RBF) to diagnose CAD using clinical data. The Z-Alizadeh Sani CAD data set available on University of California (UCI, Irvine), has been used for validating this methodology. The class imbalance problem in this data set has been resolved using Synthetic Minority OverSampling Technique (SMOTE). Relevant features are selected using the Extra Tree feature selection method. Authors have evaluated the performance of different classifiers Extreme Gradient Boosting (XGBoost), K-NN (K-Nearest Neighbour), Support Vector Machine-Linear (SVM-Linear) and Support Vector Machine-Radial Basis Function (SVM-RBF) on the data set. GridSearch optimisation method is used for hyperparameter optimisation. Accuracy of 95.16% is achieved by ET-SVMRBF which is higher than recent existing work in literature.

Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets.

The complexity and high dimensionality are the inherent concerns of big data. The role of feature selection has gained prime importance to cope with the issue by reducing dimensionality of datasets. The compromise between the maximum classification accuracy and the minimum dimensions is as yet an unsolved puzzle. Recently, Monte Carlo Tree Search (MCTS)-based techniques have been invented that have attained great success in feature selection by constructing a binary feature selection tree and efficiently focusing on the most valuable features in the features space. However, one challenging problem associated with such approaches is a tradeoff between the tree search and the number of simulations. In a limited number of simulations, the tree might not meet the sufficient depth, thus inducing biasness towards randomness in feature subset selection. In this paper, a new algorithm for feature selection is proposed where multiple feature selection trees are built iteratively in a recursive fashion. The state space of every successor feature selection tree is less than its predecessor, thus increasing the impact of tree search in selecting best features, keeping the MCTS simulations fixed. In this study, experiments are performed on 16 benchmark datasets for validation purposes. We also compare the performance with state-of-the-art methods in literature both in terms of classification accuracy and the feature selection ratio.

Exploring permissions in android applications using ensemble-based extra tree feature selection

<span>The fast development of mobile apps and its usage has led to increase the risk of exploiting user privacy. One method used in Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, that permissions could be exploited by attackers when granting certain combinations of permissions. So, the aim of this paper is to explore the pattern of malware apps based on analyzing permissions by proposing framework utilizing feature selection based on ensemble extra tree classifier method and machine learning classifier. The used dataset had 25458 samples (8643 malware apps & 16815 benign apps) with 173 features. Three dataset with 25458 samples and 5, 10 and 20 features respectively were generated after using the proposed feature selection method. All the dataset was fed to machine learning. Support Vector machine (SVM), K Neighbors Classifier, Decision Tree, Naïve bayes and Multilayer Perceptron (MLP) classifiers were used. The classifiers models were evaluated using true negative rate (TNR), false positive rate (FNR) and accuracy metrics. The experimental results obtained showed that Support Vector machine and KNeighbors Classifiers with 20 features achieved the highest accuracy with 94 % and TNR with rate of 89 % using KNeighbors Classifier. The FNR rate is dropped to 0.001 using 5 features with support vector machine (SVM) and Multilayer Perceptrons (MLP) classifiers. The result indicated that reducing permission features improved the performance of classification and reduced the computational overhead.</span>

Feature selection tree for automated machinery fault diagnosis

Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machine learning algorithm for machinery fault classification, less accurate fault classification may inadvertently result due to overfitting issues. It is therefore only by selecting the most representative features that overfitting outcomes can be avoided and classification accuracy be improved. Currently, the genetic algorithm (GA) is regarded as the most commonly used and reliable feature selection tool for the improvement of accuracy for any machine learning algorithm. However, the greatest challenge for GA is that it may fall into a local optima and be computationally demanding. To overcome this limitation, a feature selection tree (FST) is here proposed. Numerous experimental dataset feature selections were executed using FST and GA; their performance is compared and discussed. Analysis showed that the proposed FST resulted in identical or superior optimal feature subsets when compared to the renowned GA method, but with a 20-time faster simulation period. The proposed FST is therefore more efficient in performing feature selection task than GA.

Feature Selection for High Dimensional Data Using Monte Carlo Tree Search

Feature selection is the preliminary step in machine learning and data mining. It identifies the most important and relevant features within a dataset by eliminating the redundant or irrelevant features. The substantial benefits may include an improved performance in terms of high prediction accuracy, reduced computational complexity, and simply interpretable underlying models. In this paper, we present a novel framework to investigate and understand the importance of Monte Carlo tree search (MCTS) in feature selection for very high-dimensional datasets. We construct a binary feature selection tree where each node represents one of the two feature states: a feature is selected or not. The search starts with an empty root node reflecting that no feature is selected. Then, the search tree is expanded by adding nodes in an incremental fashion through MCTS-based simulations. Following tree and default policy, every iteration generates an initial feature subset, where a filter is used to select the top k features forming the candidate feature subset. The classification accuracy is used as the goodness or reward of the candidate feature subset and propagated backward up to the root node following the active path. Finally, the candidate subset with highest reward is selected as the best feature subset. Experiments are performed on 30 real-world datasets, including 14 very high-dimensional microarray datasets, and results are also compared with state-of-the-art methods in the literature, which proves the efficacy, validity, and significance of the proposed method.

Reputation Scoring Fake News Using Text Mining

The classification of hoax news or news with incorrect information is one of the text categorization applications.Like text-based categorization of machine applications in general, this system consists of pre-processing andexecution of classification models. In this study, experiments were conducted to select the best technique in each sub-process by using 1200 articles hoax and 600 articles no hoax collected manually. This research Triedexperimenting to determine the best preprocessing stages between stop removals and stemming and showing the results of the deception Tree algorithm achieving an accuracy of 100% concluded above naive byes more stable level of accuracy in the number of datasets used in all candidates. Information gain, TFIDF and GGA based on using Naive Byes algorithm, supporting Vector Machine and Decision Tree no significant percentage change occurred on all candidates. But after using GGA (Optimize Generation) feature selection there is an increase of accuracy level The results of a comparison of classification algorithms between Naive Byes, decision trees and Support Vector machines combined with the GGA feature selection method for classifying the best result is generated by the selection of GGA + Decision Tree feature on candidate 2 (Paslon2) 100% and in the selection of the Information Gain + Decision Tree Feature selection with the lowest accuracy Candidate 3 at 36.67%, but overall improvement of accuracy Occurred on all algorithm after using feature selection and Naive byes more stable level of accuracy in the number of datasets used in all candidates.

A binary tree feature selection technique for limited training sample size

In cases where there are larger numbers of features available than should be used for a given classification task, current practice is to arbitrarily pick the number of features to be used and then to use a feature selection algorithm to determine the specific feature subset to be used. An algorithm is presented that predicts the best feature dimensionality, taking into account the number of training samples. It is demonstrated that rather small training set sizes are still practical using these techniques. Several experiments are presented to assess the algorithm's performance, and a binary tree classification procedure with two examples that utilize the algorithm is shown to demonstrate its usefulness.