Classification accuracy performance of Na&amp;#x00EF;ve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1) - comparative study

Cufoglu, Ayse; Lohi, Mahi; Madani, Kambiz

doi:10.1109/icces.2008.4772998

Cited by 14 publications

(10 citation statements)

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“…5 shows that NB outperforms BN and J48 classifiers with respect to the accuracy and mean absolute error of classification of BoTySeGa gameplay data. Our result confirms the previous result [23,24] …”

Section: A the Classification Resultssupporting

confidence: 83%

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Sukajaya

Purnama

Purnomo

2015

Int. J. Emerg. Technol. Learn.

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Abstract-Lately, personalized learning approach attracted the attention of so many researchers due to its capacity to improve the quality of educational system. This approach provides opportunities to maximize the potential of all students based on their profile. This indicates the necessity of grouping learners! ! profiles appropriately in order to optimize contribution of personalized learning approach in achieving learning objective. The problems occurred when classifying learners! ! profile especially when dealing with large number of learners, restricted time to classify, and requirement of authentic data. To solve the problem, we proposed the implementation of Bloom! !s taxonomy-based serious game as an assessment tool replacing paper-based tool for the gameplay data collection. Three different methods namely: BN, NB, and J48 were implemented to obtain the highest accuracy of classification. Our study finds that the NB classifier gives the highest percentage accuracy that is 92.31%. This classifier has the similar accuracy with BN but with lower error rate. In view of the strength of agreement, the result is categorized Very Good (" " = 0.85).

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Section: A the Classification Resultssupporting

confidence: 83%

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Sukajaya

Purnama

Purnomo

2015

Int. J. Emerg. Technol. Learn.

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“…Value is assigned to class with highest match. As number of classes increases the performance of KNN increases .The number of neighbors is obtained with value of k. Implementation of KNN mechanism is easy and the debugging process is very faster [7]. As value of k decreases noise points in training set increases.…”

Section: Algorithms 31 K-nnmentioning

confidence: 99%

Iterative Pattern Matching using K- nn and Lazy Bayesian Rule

Jadhav¹,

Ade²

2015

IJCA

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“…Previous works [44], [45], [46] and [47] have been the first in the literature to present the comparison of the classification and clustering accuracy performance of different algorithms with user profiles. In [45] NB, Instance Based Learner (IBL), Bayesian Network (BN) and Lazy Bayesian Rules (LBR) classifiers were compared using a user profile dataset. Furthermore in [46], Decision Tree (DT) algorithms to be used for user profiling (i.e.…”

Section: Classification and Clustering Algorithms For User Profilingmentioning

confidence: 99%

User Profiling - A Short Review

Cufoglu¹

2014

IJCA

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In Today's technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. Different methods, techniques and algorithms have been proposed in the literature for the user profiling process. This paper aims to give an overview on the user profiling and its related concepts, and discuss the pros and cons of current methods for the future service personalization. Furthermore, it also give details about the simulations which have been carried out with well known classification and clustering algorithms with real world user profile dataset. This work is based on the doctoral thesis of the author.

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Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1) - comparative study

Cited by 14 publications

References 10 publications

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Intelligent Classification of Learner's Cognitive Domain using Bayes Net, Naïve Bayes, and J48 Utilizing Bloom's Taxonomy-based Serious Game

Iterative Pattern Matching using K- nn and Lazy Bayesian Rule

User Profiling - A Short Review

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