Adeptness Evaluation of Memory Based Classifiers for Credit Risk Analysis

Devasena, C. Lakshmi

doi:10.1109/icica.2014.39

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…When predicting, vote each tree into a more accurate and predictable category given that. The decision is made by the class with the most votes to predict the final class [28] [29]. Random forests are formed by pre-determined random features from random selection, and in each of them, one can select the most useful variables for the problem at hand, leading to reduced data dimensionality and improved model performance [3].…”

Section: Methodsmentioning

confidence: 99%

Decision Tree Analysis Approaches to Classify Sensors Data in a Water Pumping Station

Adnan Hadi,

Khalaf Hamoud,

Monther Abboud

et al. 2024

IJCDS

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Water pumping stations play a vital role in the lives of citizens, where a failure in the pumping schedule or the quality of the pumping may affect their lives. The data of the water pumping station may expose the weaknesses in the system of the station, which can be overcome using machine learning approaches. In this paper, six decision tree algorithms are examined to find the optimal one for classifying the data of water pumping stations. The main goal is to determine the fault in the sensors to control the pumping process and overcome future failures. Six algorithms, namely J48, Rep Tree, Random Forest, Decision Stump, Hoeffding Tree, and Random Tree, are examined before and after implementing the feature selection (FS) process. FS is implemented to find the most correlated sensors and remove the less correlated sensors. The FS process affects the accuracies of the algorithms and enhances the resulting accuracies of the algorithms. Random Forest and Random Tree algorithms prove their accuracy in data classification with 100% accuracy after implementing FS and removing the less correlated sensor data. The model can be used as an assistant tool for classifying and predicting the failure of a water pumping station.

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Section: Methodsmentioning

confidence: 99%

Decision Tree Analysis Approaches to Classify Sensors Data in a Water Pumping Station

Adnan Hadi,

Khalaf Hamoud,

Monther Abboud

et al. 2024

IJCDS

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“…Each of these trees develops with a self-serving sample of the original data. To achieve the best division, a variable, randomly selecting the 'm' number of variables, is searched [43]. RF measures high-level predictive parameters and provides precise and exact outcomes using a powerful artificial intelligence technique without overfitting issues [19,43,44].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…To achieve the best division, a variable, randomly selecting the 'm' number of variables, is searched [43]. RF measures high-level predictive parameters and provides precise and exact outcomes using a powerful artificial intelligence technique without overfitting issues [19,43,44]. This decreases the model's total variance and gives reliable findings [45].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Meteorological Data Fusion Approach for Modeling Crop Water Productivity Based on Ensemble Machine Learning

Elbeltagi

Srivastava

Kushwaha

et al. 2022

Water

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Crop water productivity modeling is an increasingly popular rapid decision making tool to optimize water resource management in agriculture for the decision makers. This work aimed to model, predict, and simulate the crop water productivity (CWP) for grain yields of both wheat and maize. Climate datasets were collected over the period from 1969 to 2019, including: mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (H), solar radiation (SR), sunshine hours (Ssh), wind speed (WS), and day length (DL). Five machine learning (ML) methods were applied, including random forest (RF), support vector regression (SVM), bagged trees (BT), boosted trees (BoT), and matern 5/2 Gaussian process (MG). Models implemented by MG, including Tmean, SR, WS, and DL (Model 3); Tmax, Tmin, Tmean, SR, Ssh, WS, H, and DL (Model 8); Tmean, and SR (Model 9), were found optimal (r2 = 0.85) for forecasting CWP for wheat. Moreover, results of CWP for maize showed that the BT model, a combination of SR, WS, H, and Tmin data, achieved a high correlation coefficient of 0.82 compared to others. The outcomes demonstrated several high performance ML-based alternative CWP estimation methods in case of limited climatic data supporting decision making for designers, developers, and managers of water resources.

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“…An effective method was needed to evaluate the credit risk of the customers which was discussed by Devasena [4], Gulsoy and Kulluk [5] and Huang, Liu and Ren [6]. In these papers, the author described the various supervised learning classification algorithm that was implemented on different data sets.…”

Section: Credit Risk Assessment Using Machine Learning Techniquesmentioning

confidence: 99%

“…Various metrics were used to compare different techniques. Devasena [4] discussed memory-based classifiers such as IBk classifier, Kstar classifier, and LWL classifier, which was implemented in the German credit data set. Gulsoy and Kulluk [5] performed analysis on different techniques such as Random Trees, simple CART, PART, J48, Fuzzy, and NBTrees.…”

Section: Credit Risk Assessment Using Machine Learning Techniquesmentioning

confidence: 99%

Credit Risk Assessment using Machine Learning Techniques

Aithal¹,

Jathanna²

2019

IJITEE

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Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy

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Adeptness Evaluation of Memory Based Classifiers for Credit Risk Analysis

Cited by 10 publications

References 10 publications

Decision Tree Analysis Approaches to Classify Sensors Data in a Water Pumping Station

Decision Tree Analysis Approaches to Classify Sensors Data in a Water Pumping Station

Meteorological Data Fusion Approach for Modeling Crop Water Productivity Based on Ensemble Machine Learning

Credit Risk Assessment using Machine Learning Techniques

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