The Effect of Best First and Spreadsubsample on Selection of a Feature Wrapper With Na�ve Bayes Classifier for The Classification of the Ratio of Inpatients

Wijaya, Mahendra; Saptono, Ristu; Doewes, Afrizal

doi:10.15294/sji.v3i2.7910

Cited by 4 publications

(3 citation statements)

References 6 publications

(5 reference statements)

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“…Feature selection methods can be categorized into three types, namely the filter, wrapper, and embedded methods [4]- [7]. Filter methods, such as chi-square and information gain, have low computational costs contrary to the wrapper and the embedded methods, so the running time is fast.…”

Section: Overview Of Feature Selectionmentioning

confidence: 99%

Simulation Study of Imbalanced Classification on High-Dimensional Gene Expression Data

Rochayani

Sa'adah

Astuti

2023

SJI

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Purpose: Classification of gene expression helps study disease. However, it faces two obstacles: an imbalanced class and a high dimension. The motivation of this study is to examine the effectiveness of undersampling before feature selection on high-dimensional data with imbalanced classes.Methods: Least Absolute Shrinkage and Selection Operator (Lasso), which can select features, can handle high-dimensional data modeling. Random undersampling (RUS) can be used to deal with imbalanced classes. The Classification and Decision Tree (CART) algorithm is used to construct a classification model because it can produce an interpretable model. Thirty simulated datasets with varying imbalance ratios are used to test the proposed approaches, which are Lasso-CART and RUS-Lasso-CART. The simulated data are generated from parameters of real gene expression data.Results: The simulation study results show that when the minority class accounts for more than 25% of the observation size, the Lasso-CART method is appropriate. Meanwhile, RUS-Lasso-CART is effective when the minority class size is at least 20 observations.Novelty: The novelty of this simulation study is using the RUS-Lasso-CART hybrid method to address the classification problem of high-dimensional gene expression data with imbalanced classes.

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Section: Overview Of Feature Selectionmentioning

confidence: 99%

Simulation Study of Imbalanced Classification on High-Dimensional Gene Expression Data

Rochayani

Sa'adah

Astuti

2023

SJI

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“…Then the Naïve Bayes Classifier will classify the sample data to the class that has the highest probability value [12]. The formula of the Naïve Bayes Classifier method is as follows [13]:…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier

Trihanto

Arifudin

Muslim

2017

SJI

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The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In its development journal has two formats that can be accessed by library users namely: printed format and digital format. Then from the number of published journals, not accompanied by the growing amount of information and knowledge that can be retrieved from these documents. The TF-IDF method is one of the fastest and most efficient text mining methods to extract useful words as the value of information from a document. This method combines two concepts of weight calculation that is the frequency of word appearance on a particular document and the inverse frequency of documents containing the word. Furthermore, data analysis of journal title is done by Nave Bayes Classifier method. The purpose of the research is to build a website-based information retrieval system that can help to classify and define trends from Indonesian journal titles. This research produces a system that can be used to classify journal titles in Indonesian language, with system accuracy in determining the classification of 90,6% and 9,4% error rate. The highest percentage result that became the trend of title classification was decision support system category which was 24.7%.

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“…The existence of classification and ranking in decision making can determine the quality of the data which is an important factor in the success of forecasting [1][2] [3]. The application of the Simple Multi Attribute Rating Technique (SMART) method can be used to rank the growth and development of toddlers, nutritional status of toddlers, student assessments and assessment of vulnerability to BLAST based on predetermined weights and criteria [4][5][6] [7].…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes and SMART Metods to Determine Toddler Development and Status

Dewi

Amin

2021

JAIC

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Integrated Service Post (Posyandu) is a forum used for health services carried out from and for the community under the guidance of health workers. Currently, toddlers need to be given more attention to monitor their growth and development. The number of children under five who are monitored by posyandu cadres and puskesmas officers will certainly take a lot of time and require maximum accuracy in classifying the status of each toddler. The purpose of the design of this decision support system is to assist posyandu cadres in determining the developmental decisions of active and inactive toddlers. This research is very important to assist posyandu cadres in providing proper analysis of the developmental status of each toddler. The results of this study are an application that can be used to determine the criteria for growth and development of toddlers using the Naive Bayes and SMART methods so that they can help posyandu cadres in determining the development of toddler status.

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The Effect of Best First and Spreadsubsample on Selection of a Feature Wrapper With Na�ve Bayes Classifier for The Classification of the Ratio of Inpatients

Cited by 4 publications

References 6 publications

Simulation Study of Imbalanced Classification on High-Dimensional Gene Expression Data

Simulation Study of Imbalanced Classification on High-Dimensional Gene Expression Data

Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier

Naïve Bayes and SMART Metods to Determine Toddler Development and Status

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