Voting Classifier Research Articles

Voting Classification Method for Network Traffic Prediction

The prediction analysis is the approach of data mining which is applied to predict future possibilities based on the current information. The network traffic classification is the major issue of the prediction analysis due to complex dataset. The network traffic techniques have three steps, which are pre-processing, feature extraction and classification. In the phase of pre-processing data set is collected which is processed to removed missing and redundant values. In the second phase, the relationship between attribute and target set is established. In the last phase, the technique of classification is applied for the classification. This research study has been influenced by the different intrusion threats on internet and the ways to detect them. In this research, we have studied and analyzed the famous network traffic data -NSL KDD dataset and its various features. The proposed model is a hybrid of Logistic Regression and K-nearest neighbor classifier combined using voting classifier, which aims at classifying the data into malicious and non-malicious with more accuracy than existing methods.

Forecasting of Campus Placement for Students Using Ensemble Voting Classifier

Campus placement is a measure of students’ performance in a course. A forecasting method is proposed in this paper to predict possible campus placement of any institution. Data mining and knowledge discovery processes on academic career of students are applied. Supervised machine learning technique based classifiers are used for achieving this process. It uses an ensemble approach based voting classifier for choosing best classifier models to achieve better result over other classifiers. Experimental results have indicated 86.05% accuracy of ensemble based approach which is significantly better over other classifiers.

Ensembles of Hydrophobicity Scales as Potent Classifiers for Chimeric Virus-Like Particle Solubility - An Amino Acid Sequence-Based Machine Learning Approach.

Virus-like particles (VLPs) are protein-based nanoscale structures that show high potential as immunotherapeutics or cargo delivery vehicles. Chimeric VLPs are decorated with foreign peptides resulting in structures that confer immune responses against the displayed epitope. However, insertion of foreign sequences often results in insoluble proteins, calling for methods capable of assessing a VLP candidate’s solubility in silico. The prediction of VLP solubility requires a model that can identify critical hydrophobicity-related parameters, distinguishing between VLP-forming aggregation and aggregation leading to insoluble virus protein clusters. Therefore, we developed and implemented a soft ensemble vote classifier (sEVC) framework based on chimeric hepatitis B core antigen (HBcAg) amino acid sequences and 91 publicly available hydrophobicity scales. Based on each hydrophobicity scale, an individual decision tree was induced as classifier in the sEVC. An embedded feature selection algorithm and stratified sampling proved beneficial for model construction. With a learning experiment, model performance in the space of model training set size and number of included classifiers in the sEVC was explored. Additionally, seven models were created from training data of 24–384 chimeric HBcAg constructs, which were validated by 100-fold Monte Carlo cross-validation. The models predicted external test sets of 184–544 chimeric HBcAg constructs. Best models showed a Matthew’s correlation coefficient of >0.6 on the validation and the external test set. Feature selection was evaluated for classifiers with best and worst performance in the chimeric HBcAg VLP solubility scenario. Analysis of the associated hydrophobicity scales allowed for retrieval of biological information related to the mechanistic backgrounds of VLP solubility, suggesting a special role of arginine for VLP assembly and solubility. In the future, the developed sEVC could further be applied to hydrophobicity-related problems in other domains, such as monoclonal antibodies.

Identification of Extreme Guilt and Grave Fault in Bengali Language using Machine Learning

Though huge amount of study has been done on the Bengali Language for information retrieval, but none of them deals with extreme guilt ( ) and grave fault ( ) in the Bengali Language. In this study, we have described extreme guilt ( ) and grave fault ( ). We have used three machine learning methods, such as Logistic Regression (LR), Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB) as the baseline classifiers among the baseline classifier, MNB shows the accuracy of 89%. Ensemble learning has been used to improve the baseline classifiers. We have implemented an Ada Boost algorithm and Maximum voting classification decision method depending on the results of baseline classifiers. Maximum voting and Ada-Boost algorithms have shown an accuracy of 91% and 92% respectively. We have modified the Ada-boost algorithm using Principal Component Analysis (PCA) and named it JR-Ada-Boost. It outperforms all algorithms and gives an accuracy of 94%.

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients.

Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features.Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test.Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83–0.88, P = 0.01), but not in the test cohort (AUC = 0.80–0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma.Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

These days online networking is generally utilized as the wellspring of data as a result of its ease, simple to get to nature. In any case, expending news from online life is a twofold edged sword as a result of the widespread of fake news, i.e., news with purposefully false data. Fake news is a major issue since it affects people just as society substantial. In the internet based life, the data is spread quick and subsequently discovery component ought to almost certainly foresee news quick enough to stop the dispersal of fake news. Consequently, identifying fake news via web-based networking media is a critical and furthermore an in fact testing issue. In this paper, Ensemble Voting Classifier based, an intelligent detection system is proposed to deal with news classification both real and fake tasks. Here, eleven mostly well-known machine-learning algorithms like Naive Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting, etc. are used for detection. After cross-validation, we used the best three machine-learning algorithms in Ensemble Voting Classifier. The experimental outcomes affirm that the proposed framework can accomplish to about 94.5% outcomes as far as accuracy. The other parameters like ROC score, precision, recall and F1 are also outstanding. The proposed recognition framework can effectively find the most important highlights of the news. These can also be implemented in other classification techniques to detect fake profiles, fake message, etc.

Gait recognition using histograms of temporal gradients

In this paper, we present a gait recognition method using convolutional features and histograms of temporal gradients. The method comprises three stages, namely the convolutional stage, temporal gradient stage and classification stage. In the convolutional stage, the video frames are convolved with a set of pre-learned filters. This is followed by the temporal gradient stage. In this stage, the gradient of each convolved frame in time axis is calculated. Unlike histograms of oriented gradients that accumulate the gradients in the spatial domain, the proposed histogram of temporal gradients encodes the gradients in the spatial and temporal domain. The histogram of temporal gradients captures the gradient patterns of every pixel over the temporal axis throughout the video sequence. By doing so, the feature encodes both spatial and temporal information in the gait cycle. Finally, in the classification stage, a majority voting classification with Euclidean distance is performed for gait recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods.

An Efficient Ad Click Prediction System using Machine Learning Techniques

Ad-click prediction is a learning problem that is highly related to the multi-billion-dollar ad- promoting the online advertising industry. As the number of internet users in India reached 525 million in 2019, online advertising companies are trying to influence internet usage users for promoting their business. Machine learning is a technique in which systems getting to act without any explicit programming. Currently, machine learning is pervasive today and we can use machine learning models in every research field. The accuracy of the ad-click prediction system impacts business revenue, so it is very important to build a prediction system with fewer false positives and false negatives.in this paper, we proposed an ad-click prediction system based on machine learning techniques. The dataset is taken from Kaggle. The dataset contains nine features. The goal of the model is to evaluate the probability of an online user to click on a given ad. We built a machine learning model based on these features. We applied a voting classifier on the dataset and achieved an accuracy of 98%.We used python language for implementation.

Abstract WMP28: A Novel Machine Learning Model for Rupture Prediction of Saccular Intracranial Aneurysm

Background: Deciding how to deal with unruptured intracranial aneurysms is a complex problem. The research done so far has focused on identifying a contribution of individual risk factor (hypertension, female gender, etc.). However, it is difficult to translate the feature importance of single risk factor to a treatment decision. Recently, PHASES score was presented to predict rupture criticality, however, recent studies show it can't be used in clinical settings. Methods: A retrospective data of 915 patient records from an in-house registry of saccular aneurysms from 1997 - 2017 was used. We proposed a novel ensemble learning and apriori association rule mining-based method to define aneurysmal rupture risk in terms of Rupture Criticality Index (RCI), which is a normalized score (from 1 to 10) of the probability of aneurysmal rupture for set of associated features. In order to have interpretable and clinically useful model, we used Apriori algorithm to find what features carry strong association among them in the case of ruptured aneurysms for each combination of location and size. We used voting classifier as an ensemble technique and utilize weighted average probabilities (Soft Voting) to predict the outcome. Results: Proposed ensemble method shows precision, recall and f-measure of 75% and accuracy of 76%. The normalized values of RCI were categorized as minor, mild, moderate, severe, and critical by using Jenks natural breaks method. Evaluation results shows 26 ‘critical’, 53 ‘severe’ and 49 ‘moderate’ risk factors combinations. Examples of ‘critical’ combinations (which need immediate treatment) include: patients who are African American, current smoker, having anterior communicating artery (ACoA) Tiny-sized aneurysm (8.3-14.5 mm) with RCI of 8.57; patients who are African American, Male, having ACoA medium-sized aneurysm (8.3-14.5 mm) with RCI of 9.46; African American, Paraclinoid, Tiny-sized (<4.5mm) and current smoker with RCI 8.21, etc. Conclusion: The proposed machine learning model is capable of accurately predicting rupture risk over lifetime. Validation on retrospective data shows that results are reliable. Currently we are extending this work to make it as question-answer system using Google-home/Alexa voice assistants.

Foreground Extraction and Motion Recognition Technology for Intelligent Video Surveillance

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy.

Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform.

Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.

Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier

Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques—naive Bayes, decision trees, multilayer perceptron and logistic regression—to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.

Predicting Students’ Performance of the Private Universities of Bangladesh using Machine Learning Approaches

Every year thousands of students get admitted into different universities in Bangladesh. Among them, a large number of students complete their graduation with low scoring results which affect their careers. By predicting their grades before the final examination, they can take essential measures to ameliorate their grades. This article has proposed different machine learning approaches for predicting the grade of a student in a course, in the context of the private universities of Bangladesh. Using different features that affect the result of a student, seven different classifiers have been trained, namely: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Decision Tree, AdaBoost, Multilayer Perceptron (MLP), and Extra Tree Classifier for classifying the students’ final grades into four quality classes: Excellent, Good, Poor, and Fail. Afterwards, the outputs of the base classifiers have been aggregated using the weighted voting approach to attain better results. And here this study has achieved an accuracy of 81.73%, where the weighted voting classifier outperforms the base classifiers.

Machine Learning to Predict ICU Admission, ICU Mortality and Survivors’ Length of Stay Among COVID-19 Patients: Toward Optimal Allocation of ICU Resources

Background: Coronavirus Disease (COVID-19) causes burdens to the intensive care unit (ICU). Evidence-based planning and optimal allocation of the scarce ICU resources is urgently needed but remains unaddressed. Methods: Retrospective data from 733 in-patients with laboratory-confirmed COVD-19 in Wuhan, China, as of March 18, 2020. Demographic, clinical course and laboratory were collected and analyzed using machine learning and Least Absolute Shrinkage and Selection Operator (LASSO) to build the predictive models. Findings: First, a predictive model using an ensemble learning strategy and classifier voting mechanism was built to identify the ICU admission based on ten factors identified in 909 potential predictors. It yielded a sensitivity of 0.86 and specificity of 0.82. Second, a model built on ten significant variables predicted whether patients would die despite entering the ICU with accuracy and AUC of 92% and 98% using 3-fold cross-validation. Third, a sparse regression model was built to estimate the length of stay in ICU. It yielded that the average difference between the predicted and empirical time is less than one day. Lymphocyte absolute value appeared in all prediction tasks, thus it was a very noteworthy factor. Interpretation: We identified variables and tested the accuracy to predict the need for ICU admission, death despite ICU admission, and among survivors, length of ICU stay, before patients were admitted to ICU. Our predictions provided quantitative and objective evidence for the optimal planning and allocation of ICU resources. We revealed the most predictive variables to assist clinical workflow. Funding Statement: This work was supported by the grant from the National Natural Science Foundation of China (no.61771007). Declaration of Interests: All authors declare no competing interests. Ethics Approval Statement: This study was approved by the First Affiliated Hospital of Guangxi Medical University Hospital Ethics Committee, with the informed consent being waived.

A robust voting approach for diabetes prediction using traditional machine learning techniques

The noteworthy advances in biotechnology and biomedical sciences have prompted a huge creation of information, for example, high throughput genetic information and clinical data, produced from extensive Electronic Health Records. To this end, utilization of machine learning and data mining techniques in biosciences is by and by crucial and fundamental in endeavors to change cleverly all accessible data into profitable knowledge. Diabetes mellitus is characterized as a gathering of metabolic issue applying critical weight on human health around the world. Broad research in all parts of diabetes (determination, pathophysiology, treatment, and so forth.) has prompted the age of tremendous measures of information. The point of the present examination is to direct an orderly audit of the uses of machine-learning, data mining strategies and instruments in the field of diabetes. The main theme of this work is to provide a system which can prognosticate the diabetes in patients with better accuracy. Here, eleven well-known machine-learning algorithms like Naive Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting etc. are used for detection of diabetes at an early stage. The evaluations of all the eleven algorithms are examined on various parameters like accuracy, precision, F-measure and recall. After cross-validation and hyper-tuning, the best three machine-learning algorithms are determined and then used in Ensemble Voting Classifier. The experimental results affirm that the pointed framework can accomplish to outstanding outcome of almost 86% accuracy of the Pima Indians Diabetes Database.

Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests

In this paper, we proposed Multi-model LSTM-based Pre-trained Convolutional Neural Networks (MLP-CNNs) as an ensemble majority voting classifier for the detection of plant diseases and pests. The proposed hybrid model is based on the combination of LSTM network with pre-trained CNN models. Specifically, in transfer learning, we adopted deep feature extraction from various fully connected layers of these pre-trained deep models. AlexNet, GoogleNet and DenseNet201 models are used in this work for feature extraction. The extracted deep features are then fed into the LSTM layer in order to construct a robust hybrid model for apple disease and pest detection. Later, the output predictions of three LSTM layers determined the class labels of the input images by majority voting classifier. In addition, we use an automatic scheme for determining the best choice of the network parameters of the LSTM layer. The experiments are carried out using data consisting of real-time apple disease and pest images from Turkey and the accuracy rates are calculated for performance evaluation. The experimental results show that by using the proposed ensemble combination structure, the results are comparable to, or better than, the pre-trained deep architectures.

Discriminative Eigenpixels-Based Dictionary Learning for Hyperspectral Image Classification

Sparse representation (SR) model has been applied to hyperspectral image (HSI) classification based on the observation that any spectral pixel could be approximately represented by a linear combination of several common pixels, but its discriminative ability is not deeply explored due to an insufficient description on spatial–spectral information and less emphasis on the dictionary structure. In this letter, we propose a new algorithm of HSI classification based on discriminative eigenpixels-based dictionary learning. Instead of using neighbor pixels directly, we define a new homogeneous region for each pixel, respectively, to exploit more spatial–spectral information and extract eigenpixels from homogeneous regions to preserve the essentials for each class. For the representation-based model, a discriminative eigenpixels-based dictionary is learned in homogeneous regions, where the locality of pixels is exploited to enhance the discriminative ability. Finally, we code a homogeneous region associated with a query pixel on the learned dictionary to determine its label by use of both the nearest neighbor (NN) classifier and majority voting. Experiments are conducted on four HSIs to demonstrate the effectiveness of the proposed method.

Tweets Classification on the Base of Sentiments for US Airline Companies

The use of data from social networks such as Twitter has been increased during the last few years to improve political campaigns, quality of products and services, sentiment analysis, etc. Tweets classification based on user sentiments is a collaborative and important task for many organizations. This paper proposes a voting classifier (VC) to help sentiment analysis for such organizations. The VC is based on logistic regression (LR) and stochastic gradient descent classifier (SGDC) and uses a soft voting mechanism to make the final prediction. Tweets were classified into positive, negative and neutral classes based on the sentiments they contain. In addition, a variety of machine learning classifiers were evaluated using accuracy, precision, recall and F1 score as the performance metrics. The impact of feature extraction techniques, including term frequency (TF), term frequency-inverse document frequency (TF-IDF), and word2vec, on classification accuracy was investigated as well. Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset. The results show that the proposed VC performs better than that of other classifiers. The VC is able to achieve an accuracy of 0.789, and 0.791 with TF and TF-IDF feature extraction, respectively. The results demonstrate that ensemble classifiers achieve higher accuracy than non-ensemble classifiers. Experiments further proved that the performance of machine learning classifiers is better when TF-IDF is used as the feature extraction method. Word2vec feature extraction performs worse than TF and TF-IDF feature extraction. The LSTM achieves a lower accuracy than machine learning classifiers.

CVE-assisted large-scale security bug report dataset construction method

Identifying SBRs (security bug reports) is crucial for eliminating security issues during software development. Machine learning are promising ways for SBR prediction. However, the effectiveness of the state-of-the-art machine learning models depend on high-quality datasets, while gathering large-scale datasets are expensive and tedious. To solve this issue, we propose an automated data labeling approach based on iterative voting classification. It starts with a small group of ground-truth traing samples, which can be labeled with the help of authoritative vulnerability records hosted in CVE (Common Vulnerabilities and Exposures). The accuracy of the prediction model is improved with an iterative voting strategy. By using this approach, we label over 80k bug reports from OpenStack and 40k bug reports from Chromium. The correctness of these labels are then manually reviewed by three experienced security testing members. Finally, we construct a large-scale SBR dataset with 191 SBRs and 88,472 NSBRs (non-security bug reports) from OpenStack; and improve the quality of existing SBR dataset Chromium by identifying 64 new SBRs from previously labeled NSBRs and filtering out 173 noise bug reports from this dataset. These share datasets as well as the proposed dataset construction method help to promote research progress in SBR prediction research domain.

Https://www.ijrte.org/portfolio-item/C5403098319/

Cardio vascular disease is the major death factor in the last decade. Most of the patients diagnosed with the CVD at the later stage die even though advanced treatments are given. Earlier identification of heart disease may reduce the death rate. The cost of medical diagnosis makes it perverse for the large number of people to the early fix of the disease. Machine learning and Data mining techniques are successful in medical diagnosis through non-invasive methods. In developing such models, Feature selection is very important since it affects the accuracy of the diagnosis. In this research, the feature selection is done through Extra Tree classifier method for identifying the most important feature combination for predicting the heart disease. Cleveland and Statlog datasets are used for developing and testing the model. Base classifiers such as Support Vector Machine (SVM), K Nearest Neighbour (KNN), Decision Tree, Logistic Regression, Naïve Bayes and Vote are tested over all original 13 features from datasets, 9 feature combination and 6 feature combination. It is observed that Vote classifier using 9 and 6 feature combination gives the best accuracy and F1 score.