Feature subset selection using association rule mining and JRip classifier

Shahzad, Waseem; Asad, Salman; Khan, Muhammad Asif

doi:10.5897/ijps2013.3842

Cited by 27 publications

(3 citation statements)

References 18 publications

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“…The WEKA workbench is used for classification, clustering and selection problems 37 , 67 . The four classifiers: Naïve Bayes 68 , support vector machines (SMO) 69 , the decision tree C4.5 (J48) 70 and the rule-based RIPPER (Jrip) 71 were implemented to analyse the DNA sequences.…”

Section: Methodsmentioning

confidence: 99%

Universal mtDNA fragment for Cervidae barcoding species identification using phylogeny and preliminary analysis of machine learning approach

Filip

Strzała

Stępień

et al. 2023

Sci Rep

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The aim of the study was to use total DNA obtained from bone material to identify species of free-living animals based on the analysis of mtDNA fragments by molecular methods using accurate bioinformatics tools Bayesian approach and the machine learning approach. In our research, we present a case study of successful species identification based on degraded samples of bone, with the use of short mtDNA fragments. For better barcoding, we used molecular and bioinformatics methods. We obtained a partial sequence of the mitochondrial cytochrome b (Cytb) gene for Capreolus capreolus, Dama dama, and Cervus elaphus, that can be used for species affiliation. The new sequences have been deposited in GenBank, enriching the existing Cervidae mtDNA base. We have also analysed the effect of barcodes on species identification from the perspective of the machine learning approach. Machine learning approaches of BLOG and WEKA were compared with distance-based (TaxonDNA) and tree-based (NJ tree) methods based on the discrimination accuracy of the single barcodes. The results indicated that BLOG and WEKAs SMO classifier and NJ tree performed better than TaxonDNA in discriminating Cervidae species, with BLOG and WEKAs SMO classifier performing the best.

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Section: Methodsmentioning

confidence: 99%

Universal mtDNA fragment for Cervidae barcoding species identification using phylogeny and preliminary analysis of machine learning approach

Filip

Strzała

Stępień

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In pre-processing, the policy data obtained in Subsection 3.1 was passed as input to AWK [20] for text manipulation and labeling. AWK is a Linux/Unix text manipulation utility that searches and substitutes text.…”

Section: Pre-processing Of Policy Data To Generate a Datasetmentioning

confidence: 99%

Role of Access Control in Information Security: A Security Analysis Approach

Singh¹

2023

Information Security and Privacy in the Digital World - Some Selected Topics

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Information plays a vital role in decision-making and driving the world further in the ever-growing digital world. Authorization, which comes immediately after authentication, is essential in restricting access to information in the digital world. Various access control models have been proposed to ensure authorization by specifying access control policies. Security analysis of access control policies is a highly challenging task. Additionally, the security analysis of decentralized access control policies is complex because decentralization simplifies policy administration but raises security concerns. Therefore, an efficient security analysis approach is required to ensure the correctness of access control policies. This chapter presents a propositional rule-based machine learning approach for analyzing the Role-Based Access Control (RBAC) policies. Specifically, the proposed method maps RBAC policies into propositional rules to analyze security policies. Extensive experiments on various datasets containing RBAC policies demonstrate that the machine learning-based approach can offer valuable insight into analyzing RBAC policies.

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“…This process is stopped when a subset has a place with a similar class in all the instances [31].  JRip main premise is to produce error reduction at each incremental pruning and it consists on two phases: the grow phase when it continues to add terms to the rule until it is accurate and the incrementally pruning phase of each rule [32].  CART is a tree-building technique structured as a binary recursive portioning as each node from the decision tree can be split in only two groups.…”

Section: Nomentioning

confidence: 99%

Data Mining Approach in Diagnosis and Treatment of Chronic Kidney Disease

Turiac

Zdrodowska

2022

Acta Mechanica Et Automatica

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Chronic kidney disease is a general definition of kidney dysfunction that lasts more than 3 months. When chronic kidney disease is advanced, the kidneys are no longer able to cleanse the blood of toxins and harmful waste products and can no longer support the proper function of other organs. The disease can begin suddenly or develop latently over a long period of time without the presence of characteristic symptoms. The most common causes are other chronic diseases – diabetes and hypertension. Therefore, it is very important to diagnose the disease in early stages and opt for a suitable treatment - medication, diet and exercises to reduce its side effects. The purpose of this paper is to analyse and select those patient characteristics that may influence the prevalence of chronic kidney disease, as well as to extract classification rules and action rules that can be useful to medical professionals to efficiently and accurately diagnose patients with kidney chronic disease. The first step of the study was feature selection and evaluation of its effect on classification results. The study was repeated for four models – containing all available patient data, containing features identified by doctors as major factors in chronic kidney disease, and models containing features selected using Correlation Based Feature Selection and Chi-Square Test. Sequential Minimal Optimization and Multilayer Perceptron had the best performance for all four cases, with an average accuracy of 98.31% for SMO and 98.06% for Multilayer Perceptron, results that were confirmed by taking into consideration the F1-Score, for both algorithms was above 0.98. For all these models the classification rules are extracted. The final step was action rule extraction. The paper shows that appropriate data analysis allows for building models that can support doctors in diagnosing a disease and support their decisions on treatment. Action rules can be important guidelines for the doctors. They can reassure the doctor in his diagnosis or indicate new, previously unseen ways to cure the patient.

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Feature subset selection using association rule mining and JRip classifier

Cited by 27 publications

References 18 publications

Universal mtDNA fragment for Cervidae barcoding species identification using phylogeny and preliminary analysis of machine learning approach

Universal mtDNA fragment for Cervidae barcoding species identification using phylogeny and preliminary analysis of machine learning approach

Role of Access Control in Information Security: A Security Analysis Approach

Data Mining Approach in Diagnosis and Treatment of Chronic Kidney Disease

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