Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool

Ashari, Ahmad; Paryudi, Iman; Tjoa, A Min

doi:10.14569/ijacsa.2013.041105

Cited by 100 publications

(79 citation statements)

References 23 publications

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“…Previous studies on algorithm of classification have proven that naïve bayes is the best algorithm in comparison with the other ones such of Decision Tree, Naïve Bayes, and K-NN [9]- [11]. It has also been found that accuracy and speed are the most supporting and helpful features of the algorithm in classifying data.…”

Section: Naïve Bayes Algorithmmentioning

confidence: 99%

Web Scraping and Naïve Bayes Classification for Job Search Engine

Slamet

Andrian

Maylawati

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Many organisations (government of non-government) use websites to share information of new recruitment for the workers. This information overflows on thousands of sites with various attributes and criteria. However, this availability forms a complex puzzle in the selection process and lead to inefficient runtime. This study proposes a simple method for job searching simplification through a construction and collaboration of web scraping technique and classification using Naïve Bayes on search engine. This study is resulting an effective and efficient application for users to seek a potential job that fit in with their interests.

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Section: Naïve Bayes Algorithmmentioning

confidence: 99%

Web Scraping and Naïve Bayes Classification for Job Search Engine

Slamet

Andrian

Maylawati

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The purpose of a prediction algorithm is to forecast future values based on our present records. [3] Some common tools for prediction include: neural networks, regression, Support Vector Machine (SVM), and discriminant analysis. Recently, data mining techniques such as neural networks, fuzzy logic systems, genetic algorithms and rough set theory are used to predict control and failure detection tasks [4].…”

Section: Prediction Algorithmmentioning

confidence: 99%

“…Decision tree falls under supervised learning techniques as we have known labels in the training data set in order to train the classifier [3]. The Traditional Algorithm for learning decision trees is implemented using information gain as well as using gain ratio.…”

Section: Decision Treementioning

confidence: 99%

“…Although Decision Tree classifying the data is that they are simple to understand and interpret but there are some disadvantages such as, Most of the algorithms (like ID3 and C4.5) require that the target attribute will have only discrete values and as decision trees use the "divide and conquer" method, they tend to perform well if a few highly relevant attributes exist, but less so if many complex interactions are present [3]. By studying decision tree, it is observed that decision tree will not suitable to predict chemical bond formation.…”

Section: Observationsmentioning

confidence: 99%

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Survey :Various Prediction Algorithms for Chemical Bond Formation on GPU

Kulkarni¹

2016

IJECS

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Nowadays, the persuit of prediction of chemical bond formation among researchers is evident to discover new drugs or for new discovery of chemicals. Predictive modeling gives statistics to predict outcomes. Most of time the event one wants to predict is in the future, but predictive modeling applicable to any type of event, regardless of when it occurred. In this paper, we focused on comparative analysis of various Prediction algorithms to estimate the best algorithm for the prediction of chemical bond formation with observations. The performances of these techniques are compared and it is observed that parallel Genetic Algorithm provides better performance results in accuracy and speedup as compared to other prediction techniques on GPU.

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“…Support Vector Machine and Naive Bayes Classifier are studied for various training sets and their efficiency for unknown set are analyzed for Accuracy, AUC [10], Error Rate, F-measure, Precision, Recall and Specificity [11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Advanced Classification Methods

Shruti¹

2015

IJRITCC

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Abstract-The availability of huge amounts of data leads to the need for powerful data analysis tools to extract useful knowledge. Several data mining tools exist to improve data analysis on large data sets. There are a number of data mining tools namely Classification and Regression, Association Rules, Cluster Analysis and Outlier Analysis. Classification in data mining is a form of data analysis that extracts model using a training set, whose class label is known. This model is used as a classifier and is used for predicting the class label of unknown data set. This method of working is known as supervised learning. Various types of classifiers are Support Vector Machine, Bayesian Classification, Decision Tree Induction, Artificial Neural Network, K-Nearest Neighbor and Genetic Algorithms etc. Support Vector Machine (SVM) belongs to the class of supervised learning algorithms, SVMs construct a hyperplane or set of hyperplanes in higher dimensional space that separates two classes. Naive Bayes Classifiers (NBC) are statistical classifiers and are based on Bayes Theorem. They can predict the class membership using probabilities. In this paper study of Support Vector Machine and Naive Bayes Classifier are carried out for various training sets and their efficiency for the unknown set are analyzed for Accuracy, AUC, Error Rate, F-measure, Precision, Recall and Specificity results are documented. Most of the results correlated with the literature.

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Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool

Cited by 100 publications

References 23 publications

Web Scraping and Naïve Bayes Classification for Job Search Engine

Web Scraping and Naïve Bayes Classification for Job Search Engine

Survey :Various Prediction Algorithms for Chemical Bond Formation on GPU

Comparative Study of Advanced Classification Methods

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