A hybrid FAM–CART model and its application to medical data classification

Seera, Manjeevan; Lim, Chee Peng; Tan, Shing Chiang; Loo, Chu Kiong

doi:10.1007/s00521-015-1852-9

Cited by 22 publications

(5 citation statements)

References 49 publications

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“…The general formula for calculating the specificity and sensitivity of the pedestrian data is given in the Eq. (11) and (12).…”

Section: Performance Measurementioning

confidence: 99%

“…A brief evaluation of some essential contributions to the existing literatures presented in this section. M. Seera, C. P. Lim, S. C. Tan, and C. K. Loo, [12] presented a hybrid model for data classification, namely Fuzzy ARTMAP (FAM) neural network with classification regression tree (CART). The major benefit of FAM method is stability-plasticity dilemma that affects the data based learning system.…”

Section: Related Workmentioning

confidence: 99%

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Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Kumar¹,

Gera²,

Kamakshi³

2019

IJIES

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In recent years, a number of researchers have concentrated on medical data analytics because machine intelligence in medical diagnosis is a new trend for enormous medical applications. Generally, medical datasets are massive in size, so traditional classifiers suffered from overfitting and under-fitting problem of training set. In this paper, Gradient Descent Logistic Regression (GDLR) classification method is proposed for medical data classification. The Pearson Correlation Coefficient (PCC) is used to calculate the correlation between the features. After that, Random Forest (RF) algorithm ranks the features and selects the most relevant features to improve performance of the medical data classification. The regression technique processes the features effective and analyse the feature importance based on the weight values. The Random Forest (RF) assigns the features importance in the tree structure. The random forest is used to select the features and features are applied for the GDLR to classify effectively. The GDLR method further analysis the features for effectively analysis the feature importance based on the weight values and more relevant features are identified than the RF. The experimental analysis demonstrated that the performance of GDLR algorithm achieved better than traditional methods Neural Network for Threshold Selection (NNTS) and Mean Selection (MS). The accuracy of the proposed GDLR method achieved as 97.5% in the Hepatitis dataset, while existing mean selection method has the accuracy of 82.58%.

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“…The general formula for calculating the specificity and sensitivity of the pedestrian data is given in the Eq. (11) and (12).…”

Section: Performance Measurementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Kumar¹,

Gera²,

Kamakshi³

2019

IJIES

View full text Add to dashboard Cite

show abstract

“…For evaluation, we adopt the tenfold cross-validation (CV) method. The key advantage of CV is to minimize bias with respect to random sampling during the test phase (Seera et al, 2015 ).…”

Section: Empirical Evaluationmentioning

confidence: 99%

An intelligent payment card fraud detection system

et al. 2021

Self Cite

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Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.

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“…In addition, there are masses of categorizers practiced in various scenarios. For instance, decision tree classifiers can diagnose motor fault tasks or detect breast cancer based on medical data [ 26 , 27 , 28 ]; naive Bayes algorithm has been applied as a fault classifier to investigate the status of a monoblock centrifugal pump or an engine [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

Chen

et al. 2018

Sensors

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Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

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A hybrid FAM–CART model and its application to medical data classification

Cited by 22 publications

References 49 publications

Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

An intelligent payment card fraud detection system

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

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