Rules Generation for Multimedia Data Classifying using Rough Sets Theory

Nordin, M.; Rahman, Arif; Lazim, Yuzarimi M.; Kamir, M.; Yusof, Mohd Kamir; Abidin, Zainal

doi:10.14257/ijhit.2013.6.5.19

Cited by 3 publications

(2 citation statements)

References 10 publications

(7 reference statements)

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“…Discernibility is a central point of Rough Set Theory. It uncovers a fact that says knowledge is not enough to discern multiple objects by using existing information [35]. The discernibility matrix is generated when discernibility function can be defined.…”

Section: Rough Setmentioning

confidence: 99%

Comparison of Prediction Methods for Air Pollution Data in Malaysia and Singapore

2018

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The process for analyzing and extracting useful information from a large database that employs one or more machine learning techniques is Data Mining. There are many data mining methods that can be used in a variety of data patterns. One of them is prediction modeling. This study compares several data mining performance methods for prediction such as Naïve Bayes, Random Tree, J48, and Rough Set to get the most powerful classifier to extract the knowledge of air pollution data. The parameters being used for observation in the performance of the prediction methods are correctly and incorrectly classified instances, the time taken, and kappa statistic. The experimental result reveals that Rough Set is extremely good for classifying the Air Pollutant Index (API) data from Malaysia and Singapore. Rough Set has the lowest error and the highest performance compared to other methods with the accuracy more than 97%.

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Section: Rough Setmentioning

confidence: 99%

Comparison of Prediction Methods for Air Pollution Data in Malaysia and Singapore

2018

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“…3. General steps for development of prediction model [30] In recent years, a new technique to solve the issue of unorganized large multimedia data proposed by [31]. They used RST and web services technology in the proposed model to classify and analyze data.…”

Section: A Reduct and Rules Generationmentioning

confidence: 99%

Generated rules for AIDS and e-learning classifier using rough set approach

Sulaiman

Rahim

Pranolo

2016

Int. J. Adv. Intell. Informatics

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The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL) log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson) and Genetic algorithm (GA) as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed model

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