Evaluation of feature selection using information gain and gain ratio on bank marketing classification using naïve bayes

Prasetiyo, Budi; Alamsyah, Alamsyah; Muslim, Much Aziz; Baroroh, Niswah

doi:10.1088/1742-6596/1918/4/042153

Cited by 10 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Information Gain prefers attributes with more possible values, even though features with fewer values are more informative. The gain ratio (GR), an addition to Information Gain, is used by C4.5, a method that replaces the ID3 algorithm to combat bias [21]. By using the intrinsic information (Entropy) of that characteristic, it corrects Information Gain.…”

Section: )mentioning

confidence: 99%

An Improved Feature Removal Approach for Classification of High Dimensional Feature Dataset

Hardikkumar Harishbhai Maheta

2024

jes

View full text Add to dashboard Cite

Extracting and evaluating pertinent information in a high-dimensional feature set is extremely difficult when dealing with a high-dimensional feature space. Classification methods require additional training time to generate a classification model. Every feature is not equally relevant in a high-dimensional feature collection. As a result, feature selection is a productive method for identifying vital features and eliminating unnecessary ones. Feature selection serves as a method of pre-processing before classification. It decreases the dimension of the dataset to shorten the training period required to develop a classifier. This research study aims to propose a novel feature subset selection method that establishes the relative importance of each feature using several criteria. The proposed approach ranks available features from high to low using a variety of feature-ranking approaches. Different feature ranking algorithms perform differently on the same dataset. It is challenging to obtain robust performance with just one feature ranking algorithm. To overcome this problem, we have used the Schulze rank aggregation method. The Schulze method combines multiple feature ranking techniques to assign a rank to each feature inside the dataset. This study presents an optimization strategy for heuristic search based on the backward feature removal method. It eliminates features according to the rank determined by the Schulze rank aggregation technique. In this paper, we evaluated the performance of the proposed method against the current state-of-the-art feature ranking techniques for high-dimensional feature set classification.

show abstract

Section: )mentioning

confidence: 99%

An Improved Feature Removal Approach for Classification of High Dimensional Feature Dataset

Hardikkumar Harishbhai Maheta

2024

jes

View full text Add to dashboard Cite

show abstract

“…In the feature selection process of gain ratio, improvement is achieved by considering intrinsic information of attributes. [5], [7]. In a research conducted by [8] examining the utilization of SVM, IG and IGR, an accuracy of 0.662 and AUC of 0.533 was achieved with IG, an accuracy of 0.691 and AUC of 0.584 with IGR.…”

Section: Who Declared Coronavirus Disease 2019 (Covid-19) a Global Pa...mentioning

confidence: 99%

“…Subsequently, particle velocities are updated using equation ( 6 ), which considers correction factors 𝑐 1 and 𝑐 2 along with random variables 𝑟 1 and 𝑟 2 . Alongside this velocity update, the position of each particle is modified according to equation ( 7 ), ensuring a dynamic exploration of the solution space. These integral steps collectively define the systematic operation of the PSO algorithm in its quest for optimized solutions [25].…”

Section: F Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine

Muhamad Fawwaz Akbar,

Muhammad Itqan Mazdadi,

Muliadi

et al. 2023

j.electron.electromedical.eng.med.inform

View full text Add to dashboard Cite

In the current digital era, sentiment analysis has become an effective method for identifying and interpreting public opinions on various topics, including public health issues such as COVID-19 vaccination. Vaccination is a crucial measure in tackling this pandemic, but there are still a number of people who are skeptical and reluctant to receive the COVID-19 vaccine. This public perception is largely influenced by, including information received from social media and online platforms. Therefore, sentiment analysis of the COVID-19 vaccine is one way to understand the public's perception of the COVID-19 vaccine. This research has the purpose to enhance the classification performance in sentiment analysis of COVID-19 vaccines by implementing Information Gain Ratio (IGR) and Particle Swarm Optimization (PSO) on the Support Vector Machine (SVM). With a dataset of 2000 entries consisting of 1000 positive labels and 1000 negative labels, validation was performed through a combination of data splitting with an 80:20 ratio and stratified 10-Fold cross-validation. Applying the basic SVM, an accuracy of 0.794 and an AUC value of 0.890 were obtained. Integration with Information Gain Ratio (IGR) feature selection improved the accuracy to 0.814 and an AUC of 0.907. Furthermore, through the combination of SVM based on PSO and IGR, the accuracy significantly improved to 0.837 with an AUC of 0.913. These results demonstrate that the combination of feature selection techniques and parameter optimization can enhance the performance of sentiment classification towards COVID-19 vaccines. The conclusions drawn from this research indicate that the integration of IGR and PSO positively contributes to the effectiveness and predictive capability of the SVM model in sentiment classification tasks.

show abstract

“…Feature selection gain ratio is used to reduce the dimensionality of the collected data, effectively shortening the process's time [17]. Selected features on Gain Ratio can affect accuracy performance [18].Therefore, this research uses Gain Ratio (GR) as a feature selection to optimize the LVQ 1 algorithm and see the accuracy value obtained using and not using Gain Ratio (GR) feature selection.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology

Harni,

Afrianty,

Sanjaya

et al. 2023

bits

View full text Add to dashboard Cite

One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.

show abstract

Evaluation of feature selection using information gain and gain ratio on bank marketing classification using naïve bayes

Cited by 10 publications

References 8 publications

An Improved Feature Removal Approach for Classification of High Dimensional Feature Dataset

An Improved Feature Removal Approach for Classification of High Dimensional Feature Dataset

Implementation of Information Gain Ratio and Particle Swarm Optimization in the Sentiment Analysis Classification of Covid-19 Vaccine Using Support Vector Machine

Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology

Contact Info

Product

Resources

About