Voting Classifier Research Articles

Prediction of Movie Quality via Adaptive Voting Classifier

Information retrieval from huge social web data is a challenging task for conventional search engines. Recently, information filtering recommender systems may help to find movies, however, their services are limited because of not considering movie quality aspects in detail. Prediction of movies can be improved by using the characteristics of social web content about a movie such as social-quality, tag quality, and a temporal aspect. In this paper, we have proposed to utilize several features of social quality, user reputation and temporal features to predict popular or highly rated movies. Moreover, enhanced optimization-based voting classifier is proposed to improve the performance on proposed features. Voting classifier uses the knowledge of all the candidate classifiers but ignores the performance of the model on different classes. In the proposed model, weight is assigned to each model based on its performance for each class. For the optimal selection of weights for the candidate classifiers, Genetic Algorithm is used and the proposed model is called Genetic Algorithm Voting (GA-V) classifier. After labeling the suggested features by using a fixed threshold, several classifiers like Bayesian logistic regression, Naïve Bayes, BayesNet, Random Forest, SVM, Decision Tree, LSTM and AdaboostM1 are trained on MovieLens dataset to find high-quality/popular movies in different categories. All the traditional ML models are compared with GA-V in terms of precision, recall and F1 score. The results show the significance of the proposed features and proposed GA-V classifier.

Plant disease detection using GLCM feature extractor and voting classification approach

Agriculture productivity has a large effect on the economy of the country. Agriculture productivity can be increased by the detect plant disease at an early stage. The automated techniques play an important role to detect diseases at an early stage and detection will be accurate. The automated techniques detect the disease when the symptoms start appearing on the leave of the plant. This paper presents an automated technique for plant disease detection which is based on the four operations which are pre-processing, segmentation, feature extraction, and classification. This paper also covers a literature survey of various techniques which are already proposed by the authors. The symptoms analysis of the plant leaf is done using the GLCM algorithm and classification of the disease is done using voting classification which are key aspects of this paper. The voting classification method is the combination of the decision trees, support vector machines, k nearest neighbour methods which will improvise the accuracy of the disease detection at an early stage.

A Supervised Learning-Based Approach to Anticipating Potential Technology Convergence

Technology convergence can trigger technological innovation and change. Therefore, it is required to develop an approach to predict the convergence between technology fields that did not exist in the past. It will allow a frim to preoccupy a completely new competitive advantage that is different from that of its competitors. The timely anticipation of converging technology fields allows the innovating firms to recognize the changing business developments associated with the technology convergence. A variety of researchers have presented supervised learning-based approaches to predict potential technology fields where technology convergence is taking place using patents. They have developed machine learning models which capture the associations between the past and future connections between technology classes. Although their contributions are absolutely significant, they have a limitation in that they do not consider in depth the technological properties that are outputs of technological activities performed in each technology field. To ensure that the predicted future connections between technology fields are reasonable, technological properties that can specifically imply technology convergence should be clearly reflected in the process of the supervised learning. Motivated to remedy this problem, this study proposes a supervised learning-based approach to anticipating potential technology convergence by using the link prediction results, the technological influence relationships, and the technological relevance between technology classes. Using these as input features, several classification models that predict new technology convergence are trained and a voting classifier is developed to ensemble all the models. This study is expected to contribute to identifying new technology opportunities that can be realized through technology convergence. Furthermore, this study will assist firms to reflect the identified opportunities on their technology roadmap and make business decisions to penetrate the relevant market in a timely manner.

Ensemble Classifier Technique to Predict Gestational Diabetes Mellitus (GDM)

Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels namely, format conversion, class labeling, replacement of missing values, and normalization. Besides, four ML models such as Logistic Regression (LR), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) are used for classification. In addition to the above, RF, LR, KNN and SVM classifiers are integrated to perform the final classification in which a voting classifier is also used. In order to investigate the proficiency of the proposed model, the authors conducted extensive set of simulations and the results were examined under distinct aspects. Particularly, the ensemble model has outperformed the classical ML models with a precision of 94%, recall of 94%, accuracy of 94.24%, and F-score of 94%.

Performance Evaluation of Multiple Classifiers for Predicting Fake News

The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

A Prototypical Network-Based Approach for Low-Resource Font Typeface Feature Extraction and Utilization

This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.

Radiometric Identification of Signals by Matched Whitening Transform

Radiometric identification is the problem of attributing a signal to a specific source. In this work, a radiometric identification algorithm is developed using the whitening transformation. The approach stands out from the more established methods in that it works directly on the raw IQ data and hence is featureless. As such, the commonly used dimensionality reduction algorithms do not apply. The premise of the idea is that a data set is “most white” when projected on its own whitening matrix than on any other. In practice, transformed data are never strictly white since the training and the test data differ. The Förstner-Moonen measure that quantifies the similarity of covariance matrices is used to establish the degree of whiteness. The whitening transform that produces a data set with the minimum Förstner-Moonen distance to a white noise process is the source signal. The source is determined by the output of the mode function operated on the Majority Vote Classifier decisions. Using the Förstner-Moonen measure presents a different perspective compared to maximum likelihood and Euclidean distance metrics. The whitening transform is also contrasted with the more recent deep learning approaches that are still dependent on feature vectors with large dimensions and lengthy training phases. It is shown that the proposed method is simpler to implement, requires no features vectors, needs minimal training and because of its non-iterative structure is faster than existing approaches.

Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage.

Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.

Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking

Coal gangue is a kind of industrial waste in the coal mine preparation process. Compared to conventional manual or machine-based separation technology, vision-based methods and robotic grasping are superior in cost and maintenance. However, the existing methods may have a poor recognition accuracy problem in diverse environments since coals and gangues’ apparent features can be unreliable. This paper analyzes the current methods and proposes a vision-based coal and gangue recognition model LTC-Net for separation systems. The preprocessed full-scale images are divided into n × n local texture images since coals and gangues differ more on a smaller scale, enabling the model to overcome the influence of characteristics that tend to change with the environment. A VGG16-based model is trained to classify the local texture images through a voting classifier. Prediction is given by a threshold. Experiments based on multi-environment datasets show higher accuracy and stability of our method compared to existing methods. The effect of n and t is also discussed.

Early Identification of Resuscitated Patients with a Significant Coronary Disease in Out-of-Hospital Cardiac Arrest Survivors without ST-Segment Elevation.

This study aimed to develop a machine learning (ML)-based model for identifying patients who had a significant coronary artery disease among out-of-hospital cardiac arrest (OHCA) survivors without ST-segment elevation (STE). This multicenter observational study used data from the Korean Hypothermia Network prospective registry (KORHN-PRO) gathered between October 2015 and December 2018. We used information available before targeted temperature management (TTM) as predictor variables, and the primary outcome was a significant coronary artery lesion in coronary angiography (CAG). Among 1373 OHCA patients treated with TTM, 331 patients without STE who underwent CAG were enrolled. Among them, 127 patients (38.4%) had a significant coronary artery lesion. Four ML algorithms, namely regularized logistic regression (RLR), random forest classifier (RF), CatBoost classifier (CBC), and voting classifier (VC), were used with data collected before CAG. The VC model showed the highest accuracy for predicting significant lesions (area under the curve of 0.751). Eight variables (older age, male, initial shockable rhythm, shorter total collapse duration, higher glucose and creatinine, and lower pH and lactate) were significant to ML models. These results showed that ML models may be useful in developing early predictive tools for identifying high-risk patients with a significant stenosis in CAG.

An ensembling model for early identification of at‐risk students in higher education

AbstractAudience Response Systems like clickers are gaining much attention for early identification of at‐risk students as quality education, student success rate and retention are major concerning areas, as evidenced in this COVID scenario. Usage of this active learning strategy across the varying strength of classrooms are found to be much effective in retaining the attention, retention and learning power of the students. However, implementing clickers for large classrooms incur overhead costs on instructor's part. As a result, educational researchers are experimenting with various lightweight alternatives. This paper discusses one such alternative: lightweight formative assessments for blended learning environments. It discusses their implementation and effectiveness in early identification of at‐risk students. This study validates the usage of lightweight assessments for three core pedagogically different courses of large computer science engineering classrooms. It uses voting ensemble classifier for effective predictions. With the usage of lightweight assessments in early identification of at‐risk students, accuracy range of 87%–94.7% have been achieved along‐with high ROC‐AUC values. The study also proposes the generalized pedagogical architecture for fitting in these lightweight assessments within the course curriculum of pedagogically different courses. With the constructive outcomes, the light‐weight assessments seem to be promising for efficient handling of scaling technical classrooms.

Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region

In the last decade, ground-level ozone exposure has led to a significant increase in environmental and health risks. Thus, it is essential to measure and monitor atmospheric ozone concentration levels. Specifically, recent improvements in machine learning (ML) processes, based on statistical modeling, have provided a better approach to solving these risks. In this study, we compare Naive Bayes, K-Nearest Neighbors, Decision Tree, Stochastic Gradient Descent, and Extreme Gradient Boosting (XGBoost) algorithms and their ensemble technique to classify ground-level ozone concentration in the El Paso-Juarez area. As El Paso-Juarez is a non-attainment city, the concentrations of several air pollutants and meteorological parameters were analyzed. We found that the ensemble (soft voting classifier) of algorithms used in this paper provide high classification accuracy (94.55%) for the ozone dataset. Furthermore, variables that are highly responsible for the high ozone concentration such as Nitrogen Oxide (NOx), Wind Speed and Gust, and Solar radiation have been discovered.

Building a predictive model to assist in the diagnosis of cervical cancer.

Aim: Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Materials and methods: This research presents a novel ensemble technique for predicting cervical cancer risk. Specifically, the authors introduce a voting classifier that aggregates prediction probabilities from multiple machine-learning models: logistic regression, K-nearest neighbor, decision tree, XGBoostand multilayer perceptron. Results: The average accuracy, precision, recalland f1-score of thevoting classifier were 96.6, 97.4, 95.9 and 96.6, respectively. Furthermore, the voting algorithm gains average high values for all evaluation metrics (accuracy, precision, recalland f1-score). The f1-score of the algorithm is 96%, which demonstrates the robustness of the model. Conclusion: The findings suggest that the probability of having cervical cancer can be accurately predicted utilizing the voting technique.

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study focuses on the future prediction of the effectiveness of the COVID-19 vaccine effectiveness which has been presented as a light in the dark. People bear several reservations, including concerns about the efficacy of the COVID-19 vaccine. Under these presumptions, the COVID-19 vaccine would either lower the risk of developing the malady after injection, or the vaccine would impose side effects, affecting their existing health condition. In this regard, people have publicly expressed their concerns regarding the vaccine. This study intends to estimate what perception the masses will establish about the role of the COVID-19 vaccine in the future. Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. A voting classifier was used at the end of this study to determine the accuracy of all the classifiers. The results prove that the SVM produces the best forecasting results and that artificial neural networks (ANNs) produce the worst prediction toward the individual aptitude to be vaccinated by the COVID-19 vaccine. When using the voting classifier, the proposed system provided an overall accuracy of 89.9% for the random dataset and 45.7% for the date-wise dataset. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19 vaccine.

A novel machine learning scheme for classification of medicinal herbs based on 2D-FTIR fingerprints

A proof-of-concept medicinal herbs identification scheme using machine learning classifiers is proposed in the form of an automated computational package. The scheme makes use of two-dimensional correlation Fourier Transformed Infrared (FTIR) fingerprinting maps derived from the FTIR of raw herb spectra as digital input. The prototype package admits a collection of 11 machine learning classifiers to form a voting pool. A common set of oversampled dataset containing 5 different herbal classes is used to train the pool of classifiers on a one-verses-others manner. The collections of trained models, dubbed the voting classifiers, are deployed in a collective manner to cast their votes to support or against a given inference fingerprint whether it belongs to a particular class. By collecting the votes casted by all voting classifiers, a logically designed scoring system will select out the most probable guess of the identity of the inference fingerprint. The same scoring system is also capable of discriminating an inference fingerprint that does not belong to any of the classes the voting classifiers are trained for as the ‘others’ type. The proposed classification scheme is stress-tested to evaluate its performance and expected consistency. Our experimental runs show that, by and large, a satisfactory performance of the classification scheme of up to 90 % accuracy is achieved, providing a proof-of-concept viability that the proposed scheme is a feasible, practical, and convenient tool for herbal classification. The scheme is implemented in the form of a packaged Python code, dubbed the “Collective Voting” (CV) package, which is easily scalable, maintained and used in practice.

Classification of the Trap-Neuter-Return Surgery Images of Stray Animals Using Yolo-Based Deep Learning Integrated with a Majority Voting System

Trap-neuter-return (TNR) has become an effective solution to reduce the prevalence of stray animals. Due to the non-culling policy for stray cats and dogs since 2017, there is a great demand for the sterilization of cats and dogs in Taiwan. In 2020, Heart of Taiwan Animal Care (HOTAC) had more than 32,000 cases of neutered cats and dogs. HOTAC needs to take pictures to record the ears and excised organs of each neutered cat or dog from different veterinary hospitals. The correctness of the archived medical photos and the different shooting and imaging angles from different veterinary hospitals must be carefully reviewed by human professionals. To reduce the cost of manual review, Yolo’s ensemble learning based on deep learning and a majority voting system can effectively identify TNR surgical images, save 80% of the labor force, and its average accuracy (mAP) exceeds 90%. The best feature extraction based on the Yolo model is Yolov4, whose mAP reaches 91.99%, and the result is integrated into the voting classification. Experimental results show that compared with the previous manual work, it can decrease the workload by more than 80%.

A sentiment-based risk indicator for the Mexican financial sector

We apply sentiment analysis to Twitter messages in Spanish to build a sentiment risk index for the financial sector in Mexico. We classify a sample of tweets from 2006-2019 to identify messages in response to a positive or negative shock to the Mexican financial sector, relative to merely informative ones. We use a voting classifier approach based on three different classifiers: one based on word polarities from a pre-defined dictionary, one based on a support vector machine classifier, and one based on neural networks. We find that the voting classifier outperforms each of the other classifiers when taken alone. Next, we compare our sentiment index with existing indicators of financial stress based on quantitative variables. We find that this novel index captures the impact of sources of financial stress not explicitly encompassed in quantitative risk measures, such as financial frauds, failures in payment systems, and money laundering. Finally, we show that a shock in our Twitter sentiment index correlates positively with an increase in financial market risk, stock market volatility, sovereign risk, and foreign exchange rate volatility.

Adversarial machine learning in Network Intrusion Detection Systems

Adversarial examples are inputs to a machine learning system intentionally crafted by an attacker to fool the model into producing an incorrect output. These examples have achieved a great deal of success in several domains such as image recognition, speech recognition and spam detection. In this paper, we study the nature of the adversarial problem in Network Intrusion Detection Systems (NIDS). We focus on the attack perspective, which includes techniques to generate adversarial examples capable of evading a variety of machine learning models. More specifically, we explore the use of evolutionary computation (particle swarm optimization and genetic algorithm) and deep learning (generative adversarial networks) as tools for adversarial example generation. To assess the performance of these algorithms in evading a NIDS, we apply them to two publicly available data sets, namely the NSL-KDD and UNSW-NB15, and we contrast them to a baseline perturbation method: Monte Carlo simulation. The results show that our adversarial example generation techniques cause high misclassification rates in eleven different machine learning models, along with a voting classifier. Our work highlights the vulnerability of machine learning based NIDS in the face of adversarial perturbation.

Machine learning-based model for predicting 1year mortality of hospitalized patients with heart failure.

AimsIndividual risk stratification is a fundamental strategy in managing patients with heart failure (HF). Artificial intelligence, particularly machine learning (ML), can develop superior models for predicting the prognosis of HF patients, and administrative claim data (ACD) are suitable for ML analysis because ACD is a structured database. The objective of this study was to analyse ACD using an ML algorithm, predict the 1 year mortality of patients with HF, and finally develop an easy‐to‐use prediction model with high accuracy using the top predictors identified by the ML algorithm.Methods and resultsMachine learning‐based prognostic prediction models were developed from the ACD on 10 175 HF patients from the Japanese Registry of Acute Decompensated Heart Failure with 17% mortality during 1 year follow‐up. The top predictors for prognosis in HF were identified by the permutation feature importance technique, and an easy‐to‐use prediction model was developed based on these predictors. The c‐statistics and Brier scores of the developed ML‐based models were compared with those of conventional risk models: Seattle Heart Failure Model (SHFM) and Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC). A voting classifier algorithm (ACD‐VC) achieved the highest c‐statistics among the six ML algorithms. The permutation feature importance technique enabled identification of the top predictors such as Barthel index, age, body mass index, duration of hospitalization, last hospitalization, renal disease, and non‐loop diuretics use (feature importance values were 0.054, 0.025, 0.010, 0.005, 0.005, 0.004, and 0.004, respectively). Upon combination of some of the predictors that can be assessed from a brief interview, the Simple Model by ARTificial intelligence for HF risk stratification (SMART‐HF) was established as an easy‐to‐use prediction model. Compared with the conventional models, SMART‐HF achieved a higher c‐statistic {ACD‐VC: 0.777 [95% confidence interval (CI) 0.751–0.803], SMART‐HF: 0.765 [95% CI 0.739–0.791], SHFM: 0.713 [95% CI 0.684–0.742], MAGGIC: 0.726 [95% CI 0.698–0.753]} and better Brier scores (ACD‐VC: 0.121, SMART‐HF: 0.124, SHFM: 0.139, MAGGIC: 0.130).ConclusionsThe ML model based on ACD predicted the 1 year mortality of HF patients with high accuracy, and SMART‐HF along with the ML model achieved superior performance to that of the conventional risk models. The SMART‐HF model has the clear merit of easy operability even by non‐healthcare providers with a user‐friendly online interface (https://hfriskcalculator.herokuapp.com/). Risk models developed using SMART‐HF may provide a novel modality for risk stratification of patients with HF.