Sigmis: A Feature Selection Algorithm Using Correlation Based Method

Blessie, E. Chandra; Karthikeyan, E.

doi:10.1260/1748-3018.6.3.385

Cited by 81 publications

(33 citation statements)

References 11 publications

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“…The correlation matrix method allows the effective and efficient removal of unimportant features that do not affect the target feature [38]. By computing the correlation coefficient between the input and output features, the method selects input features with high correlation coefficients by ranking them according to the correlation coefficient value.…”

Section: Correlation Matrixmentioning

confidence: 99%

Stable Hybrid Feature Selection Method for Compressor Fault Diagnosis

et al. 2021

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Faulty compressors must be detected in advance to speed up the quality control process of the compressor's performance. Machine learning models have recently been used as fault classification models to distinguish between normal and abnormal compressors, facilitating more sophisticated fault detection methods than those in the past. However, very few studies have been conducted on accurate and efficient feature selection, despite its high importance. Therefore, this study proposes a new hybrid method that combines the merits of existing feature methods, filter and wrapper methods, to obtain a stable, accurate, and efficient fault classification model. For this, three types of filtering methods with different characteristics, such as chi-square, extra tree classifier, and correlation matrix, are used to derive the high-ranked features and then create a powerful feature set consisting of their union sets. Subsequently, using the wrapper method, one combination of features with the highest classification accuracy was selected among all the combinations of features in the union feature set. Using two experimental examples and one numerical example with different types and numbers of data, the robustness and accuracy of the proposed method were verified through comparison with the existing filter methods by combining three classification models: support vector machine, K-nearest neighbor, and multi-layer perceptron.

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Section: Correlation Matrixmentioning

confidence: 99%

Stable Hybrid Feature Selection Method for Compressor Fault Diagnosis

et al. 2021

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“…After the 'amount' feature is normalized, the next step is to find the most relevant features to use. Thus, to find the uniqueness of each feature, we use the Correlation Coefficient to find the best features of the existing features [17].…”

Section: Pre-processingmentioning

confidence: 99%

Modified Focal Loss in Imbalanced XGBoost for Credit Card Fraud Detection

Trisanto¹,

Jakarta²,

Rismawati³

et al. 2021

IJIES

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The development of credit card use in Indonesia has not been matched by the security provided by credit card service providers. This resulted in significant losses both in terms of banking and customers. The difficulty in finding the characteristics of credit card fraud is one of the biggest challenges. Currently, many are developing machine learning models that can identify credit card fraud to help banks. Unfortunately, the model created is mostly biased towards the class which has more dominant data. This problem is caused by the imbalance of the data on the available dataset. In the previous research, Wang et al found the implementation of Focal loss in XGBoost improve the precision, recall of imbalanced data. However according to Qin et al, the parameter loss from Focal loss have poor judgement in several case of imbalanced data and to handle this weakness, they proposed Weighted -Cross Entropy Loss (W-CEL) loss in Focal loss. According to previous research, we propose a Modified Focal Loss method for Imbalanced XGBoost by entering another parameter from W-CEL loss to Focal Loss to improve the ability of Focal Loss. Focal loss itself is a method that is often used to give weight to classes that are often misinterpreted, so that with the use of imbalance parameters ( ) from W-CEL loss is expected to improve the weighted value of the Focal Loss. We tested our proposed method in credit card fraud dataset from Université Libre de Bruxelles (ULB) machine learning group. Our proposed method produced 100 % accuracy, 0.97 precision, 0.56 recall and 0.72 MCC score in scenario 1 with extreme imbalanced data, in scenario 2 with the mild imbalanced data, the result delivered 99% accuracy, 0.88 precision, 0.87 recall and 0.89 MCC and in scenario 3 with the medium imbalance data, the result delivered 100% accuracy, 0.97 precision score, 0.72 recall score and 0.83 MCC score. The results obtained in this study proved that the proposed method makes machine learning models valid and unbiased, especially in mild-imbalanced data. However, many improvements still need to be made to medium and extreme imbalanced data.

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“…The filter approach act independently of the learning algorithm and some evaluation criteria which are applied, assigning for each feature. The evaluation criteria are defined based on the information gain, the correlation and the distance between features [4]. Besides that, the filter approach has low computational cost compared to the wrapper approach [14,20,3].…”

Section: Keystroke Dynamics Used For Gender Predictionmentioning

confidence: 99%