NB Classifier Research Articles

Sentiment analysis of COVID-19 social media data through machine learning.

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

Predicting Mental Disorders Using Data Mining Techniques

Over the last few years, data mining makes a transition to new applications in medicine. Many people suffer from mental disorders because of absence of care in the health care sector, especially in the mental care. Lack of health records for patients of mental health, has been caused by many reasons, for example, patients’ hesitation to go the psychiatrist to follow-up their condition due high cost of examination fee, waiting time for their turn, and diagnosis inaccuracy. Classifiers are used to predict normal, addiction without disorder, addiction with disorder, or disorder without addiction patients’ cases. However, a rise in the cases of personality disorder and substance abuse. We used Naive Bayes and K-Nearest Neighbor classification technique on dataset consists of opioid addicts patients derived from the United States of America to obtain high accuracy of mental disorder prediction. The precision of the NB classifier is (87%), and the precision of KNN classifier is (92%). This paper presents a new method for diagnosing mental disorders through data mining.

Heart disease prediction using entropy based feature engineering and ensembling of machine learning classifiers

Machine Learning (ML) in the healthcare industry has newly made headlines. Several ML models developed in the state of the art frameworks with different databases. However, improvements are still required in terms of performance to bring the robustness of the ML models in accurate prediction of heart diseases. The main impetus of the work is to propose a new ML pipeline for accurate prediction of heart disease. It includes pre-processing and entropy based feature engineering (FE) approach to produce high quality features to provide better model performance. The heart disease dataset is curated by combining Cleveland, V A medical center, Hungarian and Switzerland databases over 14 common attributes. Imputing missing values (IMV) and Outliers are removed (OR) based on the relation exist between healthcare attributes and Mahalanobis distance respectively in the curated heart disease dataset (HDD). Experimental results revealed that the IMV + OR pre-processing dominates with better performance than other pre-processing methods applied for model evaluation. Analyses were carried out with different ML models where HDD is subjected to IMV + OR processing with Independent Component Analysis (ICA), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and entropy based FE (proposed). Employing proposed entropy based FE with IMV + OR pre-processing has shown remarkable improvement in respect of all metrics for NB and LR classifiers. Further, experimental results shown that the ensemble model (LR + NB) performed well under proposed pipeline, with AUC (96.8%), Accuracy (92.7%), Specificity (91.5%), Precision (92.5%) and F1 Score (0.931) which outperformed the state of the art results.

New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images

Due to false negative results of the real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test, the complemental practices such as computed tomography (CT) and X-ray in combination with RT-PCR are discussed to achieve a more accurate diagnosis of COVID-19 in clinical practice. Since radiology includes visual understanding as well as decision making under limited conditions such as uncertainty, urgency, patient burden, and hospital facilities, mistakes are inevitable. Therefore, there is an immediate requirement to carry out further investigation and develop new accurate detection and identification methods to provide automatically quantitative evaluation of COVID-19. In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass Naïve Bayes Classifier (NB). Moreover, several other classifiers such as Softmax, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are combined with CNN. The promising results of these classifiers are evaluated and presented for accuracy, precision, and F1-score metrics. NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with 98.31% accuracy, 100% precision and 98.25% F1-score and with the lowest execution time.

A robust hybrid fusion segmentation approach for automated tumor diagnosis and classification in brain MR images

In today’s world, Brain Tumor diagnosis plays a significant role in the field of Oncology. The earlier identification of brain tumors increases the compatibility of treatment of patients and offers an efficient diagnostic recommendation from medical practitioners. Nevertheless, accurate segmentation and feature extraction are the vital challenges in brain tumor diagnosis where the handling of higher resolution images increases the processing time of existing classifiers. In this paper, a new robust weighted hybrid fusion classifier has been proposed to identify and classify the tumefaction in the brain which is of the hybridized form of SVM, NB, and KNN (SNK) classifiers. Primarily, the proposed methodology initiates the preprocessing technique such as adaptive fuzzy filtration and skull stripping in order to remove the noises as well as unwanted regions. Subsequently, an automated hybrid segmentation strategy can be carried out to acquire the initial segmentation results, and then their outcomes are compiled together using fusion rules to accurately localize the tumor region. Finally, a Hybrid SNK classifier is implemented in the proposed methodology for categorizing the type of tumefaction in the brain. The hybrid classifier has been compared with the existing state-of-the-art classifier which shows a higher accuracy result of 99.18% while distinguishing the benign and malignant tumors from brain Magnetic Resonance (MR) images.

B-LSTM-NB BASED COMPOSITE SEQUENCE LEARNING MODEL FOR DETECTING FRAUDULENT FINANCIAL ACTIVITIES

Deep Learning (DL) in finance is widely regarded as one of the pillars of financial services sectors since it performs crucial functions such as transaction processing and computation, risk assessment, and even behavior prediction. As a subset of data science, DL can learn and develop from their experience, which does not require constant human interference and programming, implying that the technology will improve quickly. By loading an Ensemble Model (EM), a Deep Sequential Learning (DSL)model, and additional upper-layer EM classifier in the correct order, a new “Contained-In-Between (C-I-B)” composite structured DSL model is recommended in this article. In cases like Fraud Detection System (FDS), where the data flow comprises vectors with complex interconnected characteristics, DL models with this structure have proven to be highly efficient. Finally, by utilizing optimized transaction eigenvectors, a NB classifier is trained. This strategy is more effective than most standard approaches in identifying transaction fraud. The proposed model is evaluated for its accuracy, Recall and F-score, and the results show that the model has better performance against its counterparts.

A Sustainable Model for Emergency Medical Services in Developing Countries: A Novel Approach Using Partial Outsourcing and Machine Learning.

IntroductionUnlike Western countries, many low- and middle-income countries (LMIC), like India, have a de-centralized emergency medical services (EMS) involving both semi-government and non-government organizations. It is alarming that due to the absence of a common ecosystem, the utilization of resources is inefficient, which leads to shortage of available vehicles and larger response time. Fragmentation of emergency supply chain resources motivates us to propose a new vehicle routing and scheduling model equipped with novel features to ensure minimal response time using existing resources.Materials and MethodsThe data set of medical and fire-related emergencies from January 2018 to May 2018 of Uttarakhand State in India was provided by GVK Emergency Management and Research Institute (GVK EMRI) also known as 108 EMSs was used in the study. The proposed model integrates all the available EMS vehicles including partial outsourcing to non-ambulatory vehicles like police vans, taxis, etc., using a novel two-echelon heuristic approach. In the first stage, an offline learning model is developed to yield the deployment strategy for EMS vehicles. Seven well researched machine learning (ML) algorithms were analyzed for parameter prediction namely random forest (RF), convolutional neural network (CNN), k-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), logistic regression (LR), and linear discriminant analysis (LDA). In the second stage, a real-time routing model is proposed for EMS vehicle routing at the time of emergency, considering partial outsourcing.Results and DiscussionThe results indicate that the RF classifier outperforms the LR, LDA, SVM, CNN, CART and NB classifier in terms of both accuracy as well as F-1 score. The proposed vehicle routing and scheduling model for automated decision-making shows an improvement of 42.1%, 54%, 27.9% and 62% in vehicle assignment time, vehicle travel time from base to scene, travel time from scene to hospital, and total response time, respectively, in urban areas.

Ensemble Approach for Detection of Depression Using EEG Features.

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.

Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis

Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner. Further investigation of customer patterns helps the firm to develop efficient decisions and in turn, helps to optimize the enterprise’s business and maximizes consumer satisfaction correspondingly. To conduct an effective assessment about the customers, Naive Bayes(also called Simple Bayes), a machine learning model is utilized. However, the efficacious of the simple Bayes model is utterly relying on the consumer data used, and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied. However, in practice, the NB premise is not true in consumer data, and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results. In this work, an ensemble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classifier. In ensemble variable selection, two different strategies are applied: one is based upon data perturbation (or homogeneous ensemble, same feature selector is applied to a different subsamples derived from the same learning set) and the other one is based upon function perturbation (or heterogeneous ensemble different feature selector is utilized to the same learning set). Furthermore, the feature set captured from both ensemble strategies is applied to NB individually and the outcome obtained is computed. Finally, the experimental outcomes show that the proposed ensemble strategies perform efficiently in choosing a steady attribute set and increasing NB classification performance efficiently.

Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network.

Ovarian cancer is a serious sickness for elderly women. According to data, it is the seventh leading cause of death in women as well as the fifth most frequent disease worldwide. Many researchers classified ovarian cancer using Artificial Neural Networks (ANNs). Doctors consider classification accuracy to be an important aspect of making decisions. Doctors consider improved classification accuracy for providing proper treatment. Early and precise diagnosis lowers mortality rates and saves lives. On basis of ROI (region of interest) segmentation, this research presents a novel annotated ovarian image classification utilizing FaRe-ConvNN (rapid region-based Convolutional neural network). The input photos were divided into three categories: epithelial, germ, and stroma cells. This image is segmented as well as preprocessed. After that, FaRe-ConvNN is used to perform the annotation procedure. For region-based classification, the method compares manually annotated features as well as trained feature in FaRe-ConvNN. This will aid in the analysis of higher accuracy in disease identification, as human annotation has lesser accuracy in previous studies; therefore, this effort will empirically prove that ML classification will provide higher accuracy. Classification is done using a combination of SVC and Gaussian NB classifiers after the region-based training in FaRe-ConvNN. The ensemble technique was employed in feature classification due to better data indexing. To diagnose ovarian cancer, the simulation provides an accurate portion of the input image. FaRe-ConvNN has a precision value of more than 95%, SVC has a precision value of 95.96%, and Gaussian NB has a precision value of 97.7%, with FR-CNN enhancing precision in Gaussian NB. For recall/sensitivity, SVC is 94.31 percent and Gaussian NB is 97.7 percent, while for specificity, SVC is 97.39 percent and Gaussian NB is 98.69 percent using FaRe-ConvNN.

Classification of Book Collections Based on DDC 23 Using Text Mining Algorithm at UNIDA Gontor Library

The collection of books in a library is a means of information that has become the main actor as a supporter of the existence of a library. UNIDA Gontor library uses the 23rd edition of the Dewey Decimal Classification (DDC 23) classification system, as a reference for the classification numbering system for each book collection. However, in the classification numbering there is no automatic system that helps librarians in assigning classification numbering to each collection. So it is necessary to select a suitable model system to be applied to the automatic classification system. The data used in this study is in the form of blurb data on each collection of Indonesian public books in the UNIDA Gontor Library. In this study, four methods of text mining algorithms were applied to be tested and compared. The algorithm used in testing this research is Multinomial Nb, Logistic Regression, Random Forest, and Support Vector Classifier. From the test results, the highest accuracy results are the Support Vector Classifier algorithm of 72%, while the Logistic Regression algorithm is 69%, Random Forest 69%, and Multinomial Nb 59%. Further research is recommended to apply the support vector classifier algorithm into the UNIDA Gontor library information system.

Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier

The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification (Malignant and benign) accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of , the optimized CatBoost classifier is more accurate than the CatBoost classifier without optimization, which is . By using hybrid algorithms, SVM, RF, and NB automatically become more accurate. Furthermore, in terms of accuracy, SVM and RF () achieve equivalent and higher classification accuracy than NB (). The findings of relevant biomedical studies confirm the findings of the selected genes.

An Effective Text Classifier using Machine Learning for Identifying Tweets’ Polarity Concerning Terrorist Connotation

Terrorist groups in the Arab world are using social networking sites like Twitter and Facebook to rapidly spread terror for the past few years. Detection and suspension of such accounts is a way to control the menace to some extent. This research is aimed at building an effective text classifier, using machine learning to identify the polarity of the tweets automatically. Five classifiers were chosen, which are AdB_SAMME, AdB_SAMME.R, Linear SVM, NB, and LR. These classifiers were applied on three features namely S1 (one word, unigram), S2 (word pair, bigram), and S3 (word triplet, trigram). All five classifiers evaluated samples S1, S2, and S3 in 346 preprocessed tweets. Feature extraction process utilized one of the most widely applied weighing schemes tf-idf (term frequency-inverse document frequency).The results were validated by four experts in Arabic language (three teachers and an educational supervisor in Saudi Arabia) through a questionnaire. The study found that the Linear SVM classifier yielded the best results of 99.7 % classification accuracy on S3 among all the other classifiers used. When both classification accuracy and time were considered, the NB classifier demonstrated the performance on S1 with 99.4% accuracy, which was comparable with Linear SVM. The Arab world has faced massive terrorist attacks in the past, and therefore, the research is highly significant and relevant due to its specific focus on detecting terrorism messages in Arabic. The state-of-the-art methods developed so far for tweets classification are mostly focused on analyzing English text, and hence, there was a dire need for devising machine learning algorithms for detecting Arabic terrorism messages. The innovative aspect of the model presented in the current study is that the five best classifiers were selected and applied on three language models S1, S2, and S3. The comparative analysis based on classification accuracy and time constraints proposed the best classifiers for sentiment analysis in the Arabic language.

Myocardial Infarction Prediction Using Hybrid Machine Learning Techniques

The myocardial infarction prediction is an important task in health care domain in the current days. So, Prediction of cardiovascular diseases is a critical challenge in the area of clinical data analysis. It is difficult to predict myocardial infarction prediction by physicians with huge health records. To overcome this complexity we need to implement the automatic heard disease prediction system to notify the patient and get to recovery from the disease. Here to gaining the automatic system we are using machine learning techniques to easily performing myocardial infarction prediction. The machine learning techniques can be split into multiple types like unsupervised and supervised learning classifier. The supervised learning techniques working with structured data which is recommended to implement this classifiers. So, in this system we are using supervised learning techniques namely KNN, RF, NN, DT, NB, and SVM classifiers. To predict myocardial infarction, this system is using training dataset which is accessing from UCI ML repository. As well as this system is comparing accuracy performance between various machine learning algorithms and accuracy results with graphical presentation. This makes the accessing of the risk of the disease in the early stages and can try to save the patient without having any loss.

WITHDRAWN: An energy efficient and improved language translator with cnn based deep encoder and decoder

Abstract The attempt to identify the text in natural language processing is considered to a confronting task based on the variations encountered on the language formats like textures, fonts and illuminations. English is a common language used on Arab countries with Arabic scripts. To provide better understanding towards the tough Arabic language; this work introduces an English-Arabic text recognition with the available dataset. With the available data evaluation has been made for recognizing the text and provide with a proper meaning. To show an extensive research perspective to the investigators, experimentation is carried over the text features and classification is done with Deep learning approaches. This work presents a novel approach to extract invariant features from the input data and to detect appropriate work. So as to choose appropriate features, weight of the invariant feature is determined and provided for validation. Here, an encoder and a decoder are placed with the layer of CNN to perform computation. The work has been compared with existing approaches SVM, NB classifier to project the advantages of the proposed model. Simulation is done with MATLAB environment and the proposed model shows better trade off in identifying the machine based language translation for analyzing text.

Feature Selection with Binary Symbiotic Organisms Search Algorithm for Email Spam Detection

One method to increase classifier accuracy is using Feature Selection (FS). The main idea in the FS is reducing complexity, eliminating irrelevant information, and deleting a subset of input features that either have little information or have no information for prediction. In this paper, three efficient binary methods based on the Symbiotic Organisms Search (SOS) algorithm were presented for solving the FS problem. In the first and second methods, several S_shaped and V_shaped transfer functions were used for the binarization of the SOS, respectively. These methods were called BSOSS and BSOSV. In the third method, two new operators called Binary Mutualism Phase (BMP) and Binary Commensalism Phase (BCP) were presented for binarization of the SOS, named Efficient Binary SOS (EBSOS). The proposed methods were run on 18 standard UCI datasets and compared to the base and important meta-heuristic algorithms. The test results showed that the EBSOS method has the best performance among the three proposed methods for the binarization of the SOS. Finally, the EBSOS method was compared to the Genetic Algorithm (GA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO) Algorithm, Binary Flower Pollination Algorithm (BFPA), Binary Grey Wolf Optimizer (BGWO) Algorithm, Binary Dragonfly Algorithm (BDA), and Binary Chaotic Crow Search Algorithm (BCCSA). In addition, the EBSOS method was executed on the spam email dataset with the KNN, NB, SVM, and MLP classifiers. The results showed that the EBSOS method has better performance compared to other methods in terms of feature count and accuracy criteria. Furthermore, it was practically evaluated on spam email detection in particular.

Integrated Algorithm-based Credit Risk Assessment and Credit Decision Guidance

This paper investigates helping banks to assess the strength and creditworthiness of firms based on limited data on MSMEs, which leads to credit risk assessment and lending decisions. In the data of enterprise invoice with complete information, four indicators of annual profit, annual profit rate, annual profit growth rate and reputation rating were selected as independent variables (the last one symbolizes enterprise reputation, the other three symbolize enterprise strength), and whether to default or not as the dichotomous dependent variable, six single classifiers such as LDA, KNeighborsClassifier, NB, SVM, LR and MLP classifiers were trained with one The integrated classifier was finally selected through performance evaluation. For the data with missing reputation ratings, we use three indicators such as enterprise capital turnover, order completion rate, and effective invoicing rate from the data with complete information as features and reputation ratings as labels, and fit a classification prediction model by fisher linear discriminant, and then make predictions for the data with missing information. After excluding firms with a credit rating of D and a high risk factor, we allocated the loan amount to the total amount of the three types of firms according to their size. After fitting the equation for the relationship between APR and customer churn rate, a multi-objective nonlinear programming model was developed to solve the credit strategy with minimum customer churn rate and maximum profit as the objectives and loan amount and APR as the decision variables.

Steel strip surface inspection through the combination of feature selection and multiclass classifiers

Purpose During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects. Design/methodology/approach A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers. Findings The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database. Originality/value The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

Machine Learning and Feature Selection Approaches for Categorizing Arabic Text: Analysis, Comparison, and Proposal

This work adopts some classification approaches for categorizing Arabic text. The approaches are operated on two datasets as test-beds. A comparative study is done to evaluate the performance of the adopted classifiers. Some feature selection methods are also analyzed, investigated, and evaluated. Selecting the most significant features is important because the huge number of features may cause performance degradation for text classification. A comparative study is done among the adopted feature selection methods for classifying Arabic documents. Moreover, a modification is done on the feature selection approaches by doing amalgamation for the chosen methods. A novel method is also proposed for selecting the most appropriate features. The method is based on the semantic fusion and multiple-words (SF-MW) for constructing the features. A comparison is done among the adopted feature selection methods and the proposed one. The experimental results show that the best performance was for the SVM classifier compared to the KNN and NB classifiers. The combination among the adopted feature selection methods presents better results compared to the individual adopted ones. The proposed feature selection method (SF-MW) is promising as it reduced the features and achieved higher classification accuracy. The accuracy improvement was about 22% for the two chosen Arabic test-beds which contain 1246 and 1500 documents respectively. The proposed method is expected to be also efficient for other Arabic and English datasets.

Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements

In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points.