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    Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets by Abri K.Al., Sidhu M.S.

    Published 2025
    “…A deeper analysis revealed that LOF detected 55 unique outliers based on differences in local density, OCSVM isolated 44 unique outliers utilizing its transformed feature space, and IF identified 76 unique outliers leveraging its tree-based mechanics. …”
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    A comparative study between rough and decision tree classifiers by Mohamad Mohsin, Mohamad Farhan

    Published 2008
    “…Theoretically, a good set of knowledge should provide good accuracy when dealing with new cases.Besides accuracy, a good rule set must also has a minimum number of rules and each rule should be short as possible.It is often that a rule set contains smaller quantity of rules but they usually have more conditions.An ideal model should be able to produces fewer, shorter rule and classify new data with good accuracy.Consequently, the quality and compact knowledge will contribute manager with a good decision model.Because of that, the search for appropriate data mining approach which can provide quality knowledge is important.Rough classifier (RC) and decision tree classifier (DTC) are categorized as RBC.The purpose of this study is to investigate the capability of RC and DTC in generating quality knowledge which leads to the good accuracy.To achieve that, both classifiers are compared based on four measurements that are accuracy of the classification, the number of rule, the length of rule, and the coverage of rule.Five dataset from UCI Machine Learning namely United States Congressional Voting Records, Credit Approval, Wisconsin Diagnostic Breast Cancer, Pima Indians Diabetes Database, and Vehicle Silhouettes are chosen as data experiment.All datasets were mined using RC toolkit namely ROSETTA while C4.5 algorithm in WEKA application was chosen as DTC rule generator.The experimental results indicated that both classifiers produced good classification result and had generated quality rule in different types of model – higher accuracy, fewer rule, shorter rule, and higher coverage.In term of accuracy, RC obtained higher accuracy in average while DTC significantly generated lower number of rule than RC.In term of rule length, RC produced compact and shorter rule than DTC and the length is not significantly different.Meanwhile, RC has better coverage than DTC.Final conclusion can be decided as follows “If the user interested at a variety of rule pattern with a good accuracy and the number of rule is not important, RC is the best solution whereas if the user looks for fewer nr, DTC might be the best choice”…”
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    Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization by Razali, Mohd Norhisham, Ibrahim, Norizuandi, Hanapi, Rozita, Mohd Zamri, Norfarahzila, Abdul Manaf, Syaifulnizam

    Published 2023
    “…Data from an academic institution were collected and pre-processed by encoding relevant features before applying various machine learning predictive models. Decision tree regressor, linear regression, MLP regressor, random forest regressor, SGD regressor, voting regressor, and Xgboost regressor were employed as predictive models. …”
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