An incremental approach to feature selection using the weighted dominance-based neighborhood rough sets

Pan, Yanzhou; Xu, Weihua; Ran, Qinwen

doi:10.1007/s13042-022-01695-4

Cited by 23 publications

(3 citation statements)

References 40 publications

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“…This definition is useful within the context of feature selection because it gives a way to quantify the relevance of a feature subset with respect to the output vector [ 34 ]. This method is effective in removing variables with low relevance, simplifying the data, and improving model performance [ 35 , 36 ]. In this study, the MI was applied to measure the statistical dependence between the input variables and the output variable.…”

Section: Methodsmentioning

confidence: 99%

Utilization of machine learning for dengue case screening

Bohm,

Borges,

Silva

et al. 2024

BMC Public Health

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Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.

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Section: Methodsmentioning

confidence: 99%

Utilization of machine learning for dengue case screening

Bohm,

Borges,

Silva

et al. 2024

BMC Public Health

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“…The incremental attribute reduction method has garnered a great deal of interest because it may efficiently use the reduction results that have already been achieved, saving a significant amount of time and space [8]. There have been numerous studies on incremental rough set theory, e.g., in feature selection [63][64][65][66], incremental approximation calculation [67], incremental information [68], rule discovery [69], and case-based reasoning [70], where the incremental technique allows new data to be added without re-implementing the algorithm in a dynamic database. Nevertheless, the majority of rough set approaches fail to take into consideration the problem of many result attributes, making them ineffective and ill-suited to understanding the nature of the rules in the big data era.…”

Section: Rule Induction Based On Rough Setsmentioning

confidence: 99%

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Huang,

Liang,

Chuang

et al. 2024

Sustainability

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The United Nations Sustainable Development Goals (SDGs) agenda has stated the importance of green investment. Energy-related green investment involves intricate economic behavior and ecological objectives. Green investment definitely requires agile decisions, e.g., rule-based decisions, to respond to changes outside the country. The identification of significant rules with numerous result features and the assurance of the stability and robustness of the rules in decision-making are crucial for green energy investment. The rough set (RS) methodology works well for processing qualitative data that are difficult to examine with traditional statistical methods in order to induce decision rules. The RS methodology starts with the analysis of the limits of discernibility of a subset of objects belonging to the domain to induce rules. However, traditional RS methods cannot incrementally generate rules with outcome features when new objects are added, which frequently occurs in green energy investment with the inclusion of big data. In this paper, an intelligent RS approach is proposed. This approach effectively identifies the rules that either stay the same or are altered based on four classified cases after a new object is introduced; it is novel because it can deal with a complicated investment environment by imposing multiple outcome features, specifically when it is required to flexibly extract new decision rules via adding new data sets.

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“…Most existing methods for modeling and predicting algae-based biomass involve all attributes of the data in decision-making, and some redundant attributes may affect the result analysis. The heuristic attribute reduction algorithm based on rough set theory can effectively reduce the time complexity of high-dimensional problems [62][63][64]. It can also detect algae more efficiently and accurately.…”

Section: Introductionmentioning

confidence: 99%

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Dong,

Zuo,

Xiong

2024

PLoS ONE

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Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. The results of hydrological water quality data analysis show that the proposed method produces better results compared to using a standalone k-nearest neighbor regressor.

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An incremental approach to feature selection using the weighted dominance-based neighborhood rough sets

Cited by 23 publications

References 40 publications

Utilization of machine learning for dengue case screening

Utilization of machine learning for dengue case screening

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

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