Hybrid Information Gain And Bagging Methods In Data Classification Using Support Vector Machine

Introduction

In the world of data science, increasing classification accuracy is an important challenge, especially in the health sector. The accurate classification of data is crucial in various applications, including medical diagnosis, where errors can be serious. This study presents the implementation of information gain feature selection techniques and the Bootstrap Aggregation (Bagging) method to increase classification accuracy using the Machine Learning classification algorithm. The combination of bagging and information gain methods is expected to produce better accuracy, especially when classifying data with various classes in one object.

Background

The Support Vector Machine (SVM) is a well-known algorithm in dealing with classification problems. It has been widely used in various applications, including medical diagnosis, due to its high accuracy and robustness. However, the performance of SVM can be affected by the quality of the input data, including the selection of relevant features. Feature selection is an important step in the data preprocessing process, as it helps to reduce the dimensionality of the data and improve the accuracy of the classification model.

Methodology

The feature selection process in this study utilizes information gain techniques by setting a threshold limit of more than 0.02 for the selection of attributes. In the first step, information gain functions to assess and choose attributes based on the ranking that has been determined. The highest ranking attributes are then recommended for use in the classification process. Furthermore, the bagging method gives weights to each selected attribute, thus strengthening the role of these attributes in the classification by the SVM algorithm.

Implementation

This study also includes a binary classification scenario, which is categorizing data into two classes: Health (Health) or Sick (Sick). To process data, weka application is used by utilizing java library, while the accuracy measurement technique applied is 10-fold cross-validation. Through this approach, the results of accuracy are measured based on the average value of the resulting matrix.

Results

The final result of the study shows that the selection of attributes using information gain techniques, combined with granting value or weight through the bagging algorithm, succeeded in increasing the accuracy of SVM classification (SMO) from 77.34% to 82.14% in the diagnosis of diabetes. This increase shows the potential of this hybrid method in improving classification accuracy in various applications, especially in medical domains.

Discussion

The application of hybrid information gain and bagging techniques in data classification shows significant results. Gain information is an effective technique for filtering the most relevant attributes, thus helping in reducing data dimensions. In this case, the chosen attributes not only have a significant contribution but also high relevance to the results of classification.

Meanwhile, Bagging functions as a reinforcement mechanism that gives weight to the selected attributes, ensuring that attributes with greater contributions have a greater influence in the final classification. This approach is useful for reducing model variability and increasing classification stability, which is very important in the context of medical diagnosis, where errors can be serious.

Conclusion

Overall, this study offers valuable insights on how the combination of features selection techniques and ensemble methods can be used to increase accuracy in data classification. The result is not only relevant for disease diagnosis, but can also be applied in various other fields that require accurate data classification. Thus, this research opens the way for further development in the more effective and reliable Machine Learning Classification Methodology.

Future Work

This study provides a foundation for further research in the application of hybrid information gain and bagging techniques in data classification. Future studies can explore the use of this method in other applications, such as image classification, text classification, and recommender systems. Additionally, the study can be extended to include other feature selection techniques and ensemble methods to compare their performance with the hybrid method used in this study.

Limitations

This study has some limitations that need to be addressed in future research. Firstly, the study only used a binary classification scenario, and future studies can explore the use of this method in multi-class classification problems. Secondly, the study only used a single dataset, and future studies can explore the use of this method on other datasets to validate its performance.

Conclusion

Q: What is the main goal of this study?

A: The main goal of this study is to implement information gain feature selection techniques and the Bootstrap Aggregation (Bagging) method to increase classification accuracy using the Machine Learning classification algorithm.

Q: What is the significance of using a hybrid method in data classification?

A: The combination of bagging and information gain methods is expected to produce better accuracy, especially when classifying data with various classes in one object. This is because bagging helps to reduce model variability and increase classification stability, while information gain helps to filter the most relevant attributes.

Q: What is the role of information gain in feature selection?

A: Information gain is an effective technique for filtering the most relevant attributes, thus helping in reducing data dimensions. In this case, the chosen attributes not only have a significant contribution but also high relevance to the results of classification.

Q: How does the bagging method contribute to the hybrid method?

A: Bagging functions as a reinforcement mechanism that gives weight to the selected attributes, ensuring that attributes with greater contributions have a greater influence in the final classification. This approach is useful for reducing model variability and increasing classification stability.

Q: What is the accuracy of the SVM classification model before and after using the hybrid method?

A: The accuracy of the SVM classification model before using the hybrid method is 77.34%, while after using the hybrid method, the accuracy increases to 82.14%.

Q: What are the potential applications of the hybrid method in data classification?

A: The hybrid method can be applied in various applications, including medical diagnosis, image classification, text classification, and recommender systems.

Q: What are the limitations of this study?

A: This study has some limitations that need to be addressed in future research. Firstly, the study only used a binary classification scenario, and future studies can explore the use of this method in multi-class classification problems. Secondly, the study only used a single dataset, and future studies can explore the use of this method on other datasets to validate its performance.

Q: What are the future directions of this research?

A: This study provides a foundation for further research in the application of hybrid information gain and bagging techniques in data classification. Future studies can explore the use of this method in other applications, such as image classification, text classification, and recommender systems. Additionally, the study can be extended to include other feature selection techniques and ensemble methods to compare their performance with the hybrid method used in this study.

Q: What are the implications of this study for the field of data science?

A: This study offers valuable insights on how the combination of features selection techniques and ensemble methods can be used to increase accuracy in data classification. The result is not only relevant for disease diagnosis, but can also be applied in various other fields that require accurate data classification. Thus, this research opens the way for further development in the more effective and reliable Machine Learning Classification Methodology.