The Feasibility of Credit Using C4.5 Algorithm Based on Particle Swarm Optimization Prediction

Credit is a belief that one is given to a person or other entity which is concerned in the future will fulfill all the obligations previously agreed. The objective of research is necessary to do credit analysis to determine the feasibility of a credit crunch, through credit analysis results, it can be seen whether the customer is feasible or not. The methods are is used to predict credit worthiness is by using two models, models classification algorithm C4.5 and C4.5 classification algorithm model based Particle Swarm Optimization (PSO). After testing with these two models found that the result C4.5 classification algorithm generates a value of 90.99% accuracy and AUC value of 0.911 to the level diagnostics Classification Excellent, but after the optimization with C4.5 classification algorithm based on Particle Swarm Optimization accuracy values amounted to 91.18% and the AUC value of 0.913 to the level of diagnosis Excellent Classification. These both methods have different accuracy level of 0.18%

Analisa Data Mining untuk Prediksi Penyakit Hepatitis dengan Menggunakan Metode Naive Bayes dan Support Vector Machine

In the case of hepatitis disease prediction has been solved by a method using Support Vector Machine (SVM) .Penyakit hepatitis is an inflammatory disease of the liver due to viral infection that attacks and cause damage to cells and organs function hati.Penyakit forerunner hepatitis is a disease of the liver cancer. Attributes or variables that have as many as 20 attributes which consists of 19 attributes preditor and 1 as the output destination attribute used to differentiate the results of the examination. Invene dataset from the University of California (UCI) Machine Learning Repository 583 as the data used and replace missing after the data is used only to evaluate the data 153 SVMyang approach proposed in the study ini.Hasil simulations showed that by developing this model achieved a reduction in dimensions and identification hati.Salah cancer of the optimization algorithm is quite popular is Naïve Bayes. In this study, will be used also classification algorithm Support Vector Machine (SVM) will be used to establish a predictive classification model of hepatitis

A Review of Particle Swarm Optimization: Feature Selection, Classification and Hybridizations

Particle swarm optimization (PSO) is a recently grown, popular, evolutionary and conceptually simple but efficient algorithm which belongs to swarm intelligence category. This paper outlines basic concepts and reviews PSO based techniques with their applications to classification and feature selection along with some of the hybridized applications of PSO with similar other techniques. DOI: 10.17762/ijritcc2321-8169.16041

Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification

Credit approval is a process carried out by the bank or credit provider company. Where the process is carried out based on credit requests and credit proposals from the borrower. Credit approval is often difficult for banks or credit providers. Where the number of requests and classifications must be made on various data submitted. This study aims to enable banks or credit card issuing companies to carry out credit approval processes effectively and accurately in determining the status of the submissions that have been made. This research uses data mining techniques. This study uses a Credit Approval dataset from UCI Machine Learning, where there is a class imbalance in the dataset. 14 attributes are used as system inputs. This study uses the C4.5 and Naive Bayes algorithms where optimization is needed using Sample Bootstrapping and Particle Swarm Optimization (PSO) in the algorithm so that the results of the research produce good accuracy and are included in the good classification. After using the optimization, it produces an accuracy rate of C4.5 which is initially 85.99% and the AUC value of 0.904 becomes 94.44% with the AUC value of 0.969 and Naive Bayes which initially has an accuracy value of 83.09% with an AUC value of 0.916 to 90 , 10% with an AUC value of 0.944

Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis

Classification of Facial Emotions using Guided Particle Swarm Optimization I

This paper presents a novel approach to facial emotion detection using a modified Particle Swarm Optimization algorithm, which we called Guided Particle Swarm Optimization (GPSO). The approach involves tracking the movements of 10 Action Units (AUs) placed at appropriate points on the face of a subject and captured in video clips. Two dimensional rectangular domains are defined around each of the AUs and Particles are then defined to have a component in each domain, effectively creating a 10- dimensional search space within which particles fly in search of a solution. Since there are more than one possible target emotions at any point in time, multiple swarms are used, with each swarm having a specific emotion as its target. At each frame in the video clip, the solution of the swarm that is nearest to its target is accepted as the solution. Our results so far show the approach to have a promising success rate

PENERAPAN PARTICLE SWARM OPTIMAZATION UNTUK MENEN-TUKAN KREDIT KEPEMILIKAN RUMAH DENGAN MENGGUNAKAN ALGORITMA C4.5

In studies that have been done previously to determine ownership loan home. One of the methods of the most widely used method with a high degree of accuracy is the C4.5 algorithm. In conducting this study also used a method algorithm C4.5 and to improve the accuracy will be performed using the addition of particle swarm optimization method for the determination of credit ratings. Homeownership after testing the results obtained is a support vector machine produces a value of 91.93% accuracy and AUC value of 0.860 was then performed using particle swarm optimization method in which the attributes which originally totaled 8 predictor variables selected from eight attributes used. The results showed higher accuracy value that is equal to 94.15% and AUC value of 0.941. So as to achieve an increased accuracy of 2.22% and an increase in AUC of 0.081. By looking at the accuracy and AUC values, the algorithm of support vector machines based on particle swarm optimization and therefore is in the category of classification is very good. &nbsp