Typically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naive Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%).

A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naive Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naive Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

/pdf/predicting-diabetes-mellitus-using-smote-and-ensemble-3nthemhaj2.pdf

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

Ensemble learning combines a series of base classifiers and the final result is assigned to the corresponding class by using a majority voting mechanism. However, the base classifiers in the ensemble cannot perform equally well. Therefore, the base classifiers should be assigned different weights in order to increase classification performance. In this study, a novel Weighted Majority Voting Ensemble (WMVE) approach is proposed, which evaluates individual performances of each classifier in the ensemble and adjusts their contributions to class decision. In the proposed weight adjustment model, only reward mechanism is provided, so punishment is not included. Classifiers that correctly classify observations which are not correctly classified by most of the classifiers gain more weights in the ensemble. In the experimental studies, increasing value of weight was calculated for each classifier in a heterogeneous collection of classification algorithms, including C4.5 decision tree, support vector machine, k-nearest neighbor, k-star, and naive Bayes. The proposed method (WMVE) was compared with the Simple Majority Voting Ensemble (SMVE) approach in terms of classification accuracy on 28 benchmark datasets. The effectiveness of the proposed method is confirmed by the experimental results.

A Weighted Majority Voting Ensemble Approach for Classification

The proliferation of user-generated content on social media has made opinion mining an arduous job. As a microblogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate stability of the proposed approach on two more datasets, one binary and other multi-class dataset and achieved robust results.

/pdf/emotion-recognition-by-textual-tweets-classification-using-28ortxxhau.pdf

Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

Bagging is a popular method used to increase the accuracy of classification, by training a set of classifiers on slightly different datasets and aggregating their output by voting. Usually, the majority voting is used for this purpose, or the plurality voting, when the problem has multiple class values. In this study, we analyze the influence of several voting methods on the performance of two classification algorithms used for datasets with different levels of difficulty. The results reveal that the single transferable vote can be a good alternative to plurality voting, although it has the drawback of a higher computational cost related to the calculation of preference ordering.

Evaluating the effect of voting methods on ensemble-based classification

Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality.

/pdf/a-weighted-voting-classifier-based-on-differential-evolution-43upmt1xie.pdf

A Weighted Voting Classifier Based on Differential Evolution

Effect of loading-induced damage on chloride ingress behavior of recycled aggregate concrete: A comprehensive review

Effect of hydromagnesite on the hydration of hydratable alumina and properties of corundum-based castables

The on-site industrial application of hydratable alumina (HA)-bonded castables is inhibited by the high hydration rate of HA. In this study, the hydration behavior of HA co-ground with sheetlike Mg–Al hydrotalcite (MAT) is investigated. The properties of castables bonded with MAT-bearing HA are systematically assessed. The hydration rate of HA co-ground MAT decreases as this allows MAT sheets to be effectively inserted into the microcracks of HA particles during grinding, thus decreasing the direct contact area between HA and water. The strength of MAT-bearing castables (0.5 and 1 wt%) fired at 800°C improved slightly owning to the generation of magnesia–alumina spinel. The mechanical strength of castables fired at 1100 and 1550°C decreased as the MAT content increased owing to an increase in porosity. Based on an analysis of the hydration behavior of HA and the properties of HA-bonded castables, the optimal MAT/HA weight ratio is approximately 1/10. 

Controlling hydration of hydratable alumina via co‐grinding with Mg–Al hydrotalcite

Alumina-chromia/aluminum-chromium/(AC) slag, is basically treated as hazardous solid waste. The present work reports the preparation of (Al,Cr)2O3-Mg(Al,Cr)2O4-Ca(Al,Cr)12O19 composite refractory which is directly applicable to AC slag using reactive MgO as binder. Microstructure, mechanical properties and slag corrosion resistance to CaO-Al2O3-Fe2O3-SiO2-based slag of the prepared composite refractory were investigated. The results indicated that the obtained composites using AC slag aggregates exhibit superior corrosion resistance against the CaO-Al2O3-Fe2O3-SiO2-based slag compared with those with both AC slag aggregates and powders. Composites prepared using AC slag aggregates bonded with 5 wt% reactive MgO exhibited sufficient mechanical properties. This makes AC slag aggregates a possible candidate to prepare (Al,Cr)2O3-Mg(Al,Cr)2O4- Ca(Al,Cr)12O19 composite refractory used in iron- and steel-making process. 

Microstructure, mechanical property and slag corrosion resistance of (Al,Cr)2O3-Mg(Al,Cr)2O4-Ca(Al,Cr)12O19 composite refractory using alumina-chromia slag

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