Search Results - regression ((bayes algorithm) OR (based algorithm))

Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms by Sirajun Noor, Noor Azmiya

“…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. In this paper, single classifiers such as Support Vector Machine, Naïve Bayes, Bayes Net, Decision Stump, k – Nearest Neighbors, Logistic Regression, Multilayer Perceptron and Decision Tree is experimented. …”
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Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms by Nilam Nur Amir, Sjarif, Yee, Fang Lim, NurulHuda, Mohd Firdaus Azmi, Kamalia, Kamardin, Doris Wong, Hooi Ten, Hafiza, Abas, Mubarak-Ali, Al-Fahim

“…This paper presents a comparison of three different classification algorithms namely NaiveBayes (NaiveBayes classifier), Logistic Regression (Logistic classifier) and C4.5 decision tree (J48 classifier) for bankruptcy classification analysis. …”
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Classification of Diabetes Mellitus using Ensemble Algorithms by Noor, N.A.B.S., Elamvazuthi, I., Yahya, N.

“…The objective of this study is to perform DM classification using various machine learning algorithms. In this paper, individual classifiers such as Support Vector Machine, Naïve Bayes, Bayes Net, Decision Stump, k - Nearest Neighbors, Logistic Regression, Multilayer Perceptron and Decision Tree are experimented. …”
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An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset / Sarthak Rastogi ... [et al.] by Rastogi, Sarthak, Shrotriya, Archit, Singh, Mitul Kumar, Potukuchi, Raghu Vamsi

“…To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. …”
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Alternative Relative Discrimination Criterion Feature Ranking Technique for Text Classification by ABDULKAREM ALSHALIF, SARAH, SENAN, NORHALINA, SAEED, FAISAL, WAD GHABAN, WAD GHABAN, IBRAHIM, NORAINI, MUHAMMAD AAMIR, MUHAMMAD AAMIR, WAREESA SHARIF, WAREESA SHARIF

“…In text classification challenges, FS algorithms based on a ranking approach are employed to improve the classification performance. …”
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Evaluating machine learning algorithms for sentiment analysis: a comparative study to support data-driven decision making by Mohamad Daud, Nur Hafiza, Shafii, Nor Hayati, Md Nasir, Diana Sirmayunie, Fauzi, Nur Fatihah

“…Specifically, the selected algorithms—Bernoulli Naive Bayes, Linear Support Vector Machines, and Logistic Regression—are employed to train the dataset. …”
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Prediction of stroke disease using machine learning techniques / Syarifah Adilah Mohamed Yusoff ... [et al.] by Mohamed Yusoff, Syarifah Adilah, Warris, Saiful Nizam, Abu Bakar, Mohd Saifulnizam, Kadar, Rozita

“…The five models were Decision Tree, Logistic Regression, Linear Discriminant Analysis, Gaussian Naïve Bayes and Support Vector Machine, have being implemented to predict binary outcome of stroke and no stroke. …”
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Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…This model combines a rule-based approach of sentiment analysis and a supervised machine learning algorithm to classify the text. …”
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Prediction of COVID-19 patient using supervised machine learning algorithm by M., Buvana, K., Muthumayil

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An Automated System For Classifying Conference Papers by Ngan, Seon Choon Han

“…The Support Vector Machine algorithm resulted in the highest F-measure (0.906), followed closely by Logistic Regression (0.903), Random Forest (0.891), Naïve Bayes (0.880), K-Nearest Neighbour (0.831) and lastly, Decision Tree (0.778). …”
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Prediction of employee promotion using hybrid sampling method with machine learning architecture / Shahidan Shafie, Soek Peng Ooi and Khai Wah Khaw by Shafie, Shahidan, Soek, Peng Ooi, Khai, Wah Khaw

“…The first 5, 8, and 12 features are selected based on the RFI-ECT to train the machine learning algorithms. …”
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An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

“…The algorithms include Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayes (NB), Classification And Regression Tree (CART), Random Forest (RF). …”
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Graduates employment classification using data mining approach by Ab Aziz, Mohd Tajul Rizal, Yusof, Yuhanis

“…Data Mining is a platform to extract hidden knowledge in a collection of data.This study investigates the suitable classification model to classify graduates employment for one of the MARA Professional College (KPM) in Malaysia.The aim is to classify the graduates into either as employed, unemployed or further study.Five data mining algorithms offered in WEKA were used; Naïve Bayes, Logistic regression, Multilayer perceptron, k-nearest neighbor and Decision tree J48.Based on the obtained result, it is learned that the Logistic regression produces the highest classification accuracy which is at 92.5%. …”
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Predicting Customer Behaviour on Buying Life Insurance using Machine Learning by Fabu Ayub A Ghafar, Norhana Abd Rahim, Yumn Suhaylah Yusoff

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Sentiment analysis for online fashion clothing review using Support Vector Machine (SVM) algorithm / Muhammad Hakam Musawi Mohd Hamdi by Mohd Hamdi, Muhammad Hakam Musawi

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Detecting Malware with Classification Machine Learning Techniques by Mohd Yusof, Mohd Azahari, Abdullah, Zubaile, Hamid Ali, Firkhan Ali, Mohamad Sukri, Khairul Amin, Shaker Hussain, Hanizan

“…The study assesses the effectiveness of several algorithms, including Naïve Bayes, Support Vector Machine (SVM), KNearest Neighbor (KNN), Decision Tree, Random Forest, and Logistic Regression, through an examination of a publicly accessible dataset featuring both benign files and malware. …”
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Detecting Malware with Classification Machine Learning Techniques by Mohd Yusof, Mohd Azahari, Abdullah, Zubaile, Hamid Ali, Firkhan Ali, Mohamad Sukri, Khairul Amin, Shaker Hussain, Hanizan

“…The study assesses the effectiveness of several algorithms, including Naïve Bayes, Support Vector Machine (SVM), KNearest Neighbor (KNN), Decision Tree, Random Forest, and Logistic Regression, through an examination of a publicly accessible dataset featuring both benign files and malware. …”
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Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach by Bahadar A., Kanthasamy R., Sait H.H., Zwawi M., Algarni M., Ayodele B.V., Cheng C.K., Wei L.J.

“…Biomass; Coal; Complex networks; Errors; Forecasting; Gasification; Hydrogen production; Learning algorithms; Mean square error; Neural networks; Regression analysis; Sensitivity analysis; Support vector machines; Co-gasification; Gaussian process regression; Hydrogen-rich syngas; Machine learning algorithms; Machine-learning; Neural-networks; Process parameters; Regression model; Support vectors machine; Syn gas; Synthesis gas; coal; hydrogen; synfuel; biomass; chemical reaction; detection method; hydrogen; machine learning; multicriteria analysis; algorithm; Article; artificial neural network; biomass; controlled study; gasification; Gaussian processing regression; linear regression analysis; machine learning; mean absolute error; mean square error; parameters; prediction; root mean square error; sensitivity analysis; support vector machine; temperature; Bayes theorem; biomass; Bayes Theorem; Biomass; Coal; Hydrogen; Temperature…”

Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study by Qureshi, M.A., Asif, M., Hassan, M.F., Abid, A., Kamal, A., Safdar, S., Akber, R.

“…Nine Machine Learning algorithms - Naïve Bayes, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Artificial Neural Networks, Convolutional Neural Network, Recurrent Neural Networks, ID3 and Gradient Boost Tree, are attempted. …”
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Fraud detection in shipping industry based on location using machine learning comparison techniques by Ganesan Subramaniam, Mr.

“…A number of popular existing algorithms were used to execute the model developed in Rapid tool such as Naïve Bayes , Neural Net , Deep Learning, Decision Tree, Logistic Regression, SVM and k-NN. …”