NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM – ADVANTAGES AND DISADVANTAGES
Neli Kalcheva, Maya Todorova, Ginka Marinova
2020International Scientific Conference ERAZ. Knowledge Based Sustainable Development24 citationsDOIOpen Access PDF
Abstract
The purpose of the publication is to analyse popular classification algorithms in machine learning. The following classifiers were studied: Naive Bayes Classifier, Decision Tree and AdaBoost Ensemble Algorithm. Their advantages and disadvantages are discussed. Research shows that there is no comprehensive universal method or algorithm for classification in machine learning. Each method or algorithm works well depending on the specifics of the task and the data used.
Topics & Concepts
AdaBoostComputer scienceArtificial intelligenceNaive Bayes classifierDecision treeMachine learningEnsemble learningClassifier (UML)Bayes classifierStatistical classificationDecision tree learningID3 algorithmPattern recognition (psychology)Incremental decision treeAlgorithmSupport vector machineData Mining Algorithms and ApplicationsTime Series Analysis and ForecastingNeural Networks and Applications