Discretisation of conditions in decision rules induced for continuous data

Stańczyk, Urszula; Zielosko, Beata; Baron, Grzegorz

doi:10.1371/journal.pone.0231788

Cited by 11 publications

(7 citation statements)

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“…The discretisation employed in the research was used in two ways: as a pre-processing stage of input data, to obtain discrete datasets that were next explored, which is the most popular approach, but also to transform patterns discovered in the continuous domain [ 39 ]. This latter methodology is available when a data exploration process returns the mined knowledge in some easily accessible form, such as sets of decision rules.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The data and the conditions in the rules were next discretised. Such processing enabled studying the impact of various discretisation methods at significantly lower costs [ 39 ]. This proposed methodology was exploited as one of the research paths in the experiments described in the paper, while the other was by standard discretisation followed by data mining.…”

Section: Preliminariesmentioning

confidence: 99%

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Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features

Stańczyk

2022

Entropy

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Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts—a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features

Stańczyk

2022

Entropy

Self Cite

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“…Data sampling and cleaning can improve the correctness and efficiency of the model. 354 , 355 Then, it is necessary to determine the target problem according to the clinical needs and select the appropriate learning algorithms for the task. Commonly used data mining methods include classification, clustering, and association rule learning.…”

Section: Non-pathogen-based Laboratory Findings For Covid-19 Management: Prevention Diagnosis and Treatmentmentioning

confidence: 99%

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

Meng

Guo

Zhou

et al. 2021

Sig Transduct Target Ther

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The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.

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“…2) Discretization: Discretization is a method which converting continuous data into categorical data [27]. For this method, data form three attributes were processed using depth equal frequency method.…”

Section: ) Income Class Level A) Generate Income Classmentioning

confidence: 99%

Household Overspending Model Amongst B40, M40 and T20 using Classification Algorithm

Othman¹,

Bakar²,

Sani³

et al. 2020

IJACSA

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The family economy is a critical indicator of the well-being of a family institution. It can be seen by the total income and how well the household finances is managed. In Malaysia, the household income level is categorized as B40, M40 and T20. These categories can also indicate the poverty level of the household. Overspending is a phenomenon where the monthly expenses are more than the household's total income, which affects economic wellbeing. Finding important factors that affect the spending patterns among the household can reveal the causes of overspending. It will assist the government in mitigating such problems. Availability of 4 million household expenditure records obtained from the survey conducted in 2016 by the Department of Statistics Malaysia eases the aim of this study to develop a household overspending model by using machine learning. The model is developed using 12 household demographic attributes with 14451 household records. The attributes are the number of households, area, state, strata, race, highest certificate, marital status, gender, housing, income, total expenditure, and category as attributes class. The model development employs five machine learning algorithms namely decision tree, Naïve Bayes, Neural network, Support Vector Machines, Nearest Neighbour. The results show that the decision tree through J48 algorithm has produced the easiest rule to be interpreted. The model shows four attributes which were income, state, races and number of households that highly influence the overspending problem. Based on the research finding, it can be concluded that these attributes are essential for improving the indicator measure for Malaysian Family Wellbeing Index in the aspect of overspending.

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Discretisation of conditions in decision rules induced for continuous data

Cited by 11 publications

References 50 publications

Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features

Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

Household Overspending Model Amongst B40, M40 and T20 using Classification Algorithm

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