Performance evaluation of least-squares probabilistic classifier for corporate credit rating classification problem

Saito, Miho; Yamanaka, S.

doi:10.14495/jsiaml.13.9

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…It is calculated as the harmonic mean of precision and recall, with values ranging from 0 to 1, where 1 indicates perfect precision and recall. The equation for the F1 score is given by Saito et al [ 47 ]:

where precision is the proportion of true positives among all instances classified as positive, and recall is the proportion of true positives among all instances that are positive. The F1 score is useful when the classes in the dataset are imbalanced, meaning there are more instances of one class than the other.…”

Section: Resultsmentioning

confidence: 99%

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Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Chellappan

Rajaguru

2023

Diagnostics

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Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment. The primary objective of this study is to propose a novel approach for identifying type II diabetes mellitus using microarray gene data. Specifically, our research focuses on the performance enhancement of methods for detecting diabetes. Four different Dimensionality Reduction techniques, Detrend Fluctuation Analysis (DFA), the Chi-square probability density function (Chi2pdf), the Firefly algorithm, and Cuckoo Search, are used to reduce high dimensional data. Metaheuristic algorithms like Particle Swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Seven classifiers, Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), and Support Vector Machine—Radial Basis Function (SVM-RBF), are utilized to classify the diabetic and non-diabetic classes. The classifiers’ performances are analyzed through parameters such as accuracy, recall, precision, F1 score, error rate, Matthews Correlation Coefficient (MCC), Jaccard metric, and kappa. The SVM (RBF) classifier with the Chi2pdf Dimensionality Reduction technique with a PSO feature selection method attained a high accuracy of 91% with a Kappa of 0.7961, outperforming all of the other classifiers.

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Section: Resultsmentioning

confidence: 99%

Section: Precisionmentioning

confidence: 99%

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Chellappan

Rajaguru

2023

Diagnostics

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“…In this case, the weighing its distance between the samples of training to the test in a specific class or group of data. If the train set is denoted as 𝑍 𝑍 (36) Here ℎ 𝐾 represents the distance between 𝑖 class and the test samples. 𝜆 0, in order to valid the penalty cost.…”

Section: Softmax Discriminant Classifier (Sdc)mentioning

confidence: 99%

“…It is calculated as the harmonic mean of precision and recall, with values ranging from 0 to 1, where 1 indicates perfect precision and recall. The equation for F1 score is given by Saito et al [36] 𝟏 𝟐 * 𝑻𝑷 𝟐 * 𝑻𝑷 𝑭𝑷 𝑭𝑵 (45) Precision represents the ratio of true positives to the total number of instances classified as positive, while recall represents the ratio of true positives to all instances that are genuinely positive. The F1 score is useful when the classes in the dataset are imbalanced, meaning there are more instances of one class than the other.…”

Section: F1 Scorementioning

confidence: 99%

Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance

Chellappan¹,

Rajaguru²

2023

Preprint

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Diabetes becomes a life threatening non-communicable disease in the world, according to International Diabetic Federation (IDF) in 2023, an estimated 537 million adults (20-79 years) are living with diabetes, which is equivalent to 9.3% of the global adult population. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is a persistent metabolic condition marked by increased levels of glucose in the bloodstream. It is a significant global health concern, affecting millions of individuals worldwide. It having the symptoms of drowsiness for the whole day if not properly examined or treated well. It is targeted to spread over the younger age community and the number is growing in the exponential fashion. Even day to day many advancements have come from the researcher to diagnose, to find solutions to prevent from newer entry. Authors have taken a dataset with 57 non diabetic and 20 diabetic patients with the total 28735 micro array gene to undergone pre-processing process and reduced up to 22960 gene data using Dimensionality Reduction (DR) such as Detrend Fluctuation Analysis (DFA), Chi square probability density function (Chi2PDF), Firefly algorithm Cuckoo search were used in this research. Meta heuristic algorithms like Particle swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Further seven classification techniques such as Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), Support Vector Machine – Radial Basis Function (SVM-RBF) are using to make a decision, predictive analysis and segregate the data according to the level of blood glucose as Diabetic Patient (DP) and Non-Diabetic Patient (NDP).

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Transformed Graph Attention for Credit Rating

Liu,

Xiang,

Cheng

et al. 2023

2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)

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Performance evaluation of least-squares probabilistic classifier for corporate credit rating classification problem

Cited by 5 publications

References 19 publications

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance

Transformed Graph Attention for Credit Rating

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