Determination Efficient Classification Algorithm for Credit Card Owners: Comparative Study

2021 ◽  
Vol 39 (1B) ◽  
pp. 21-29
Author(s):  
Raghad A. Azeez
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Financial Institution ◽  
Classification Systems ◽  
Detection Rates ◽  
Business World ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Artificial Neural

Today in the business world, significant loss can happen when the borrowers ignore paying their loans. Convenient credit-risk management represents a necessity for lending institutions. In most times, some persons prefer to late their monthly payments, otherwise, they may face difficulties in the loan payment process to the financial institution. Mainly, most fiscal organizations are considered managed and refined client classification systems, scanning a valid client from invalid ones. This paper produces the data mining idea, specifically the classification technique of data mining and builds a system of data mining process structure. The credit scoring problem will be applied using the Taiwan bank dataset. Besides that, three classification methods are adopted, Naïve Bayesian, Decision Tree (C5.0), and Artificial Neural Network. These classifiers are implemented in the WEKA machine learning application. The results show that the C5.0 algorithm is the best among them, it achieves 0.93 accuracy rates, 0.94 detection rates, 0.96 precision rates, and 0.95 F-Measure which is higher than Naïve Bayesian and Artificial Neural Network; also, the False Positive Rate in C5.0 algorithm achieves 0.1 which is less than Artificial Neural Network and Naïve Bayesian.

Classification Methods in the Detection of New Suspicious Emails

2008 ◽  
Vol 07 (03) ◽  
pp. 209-217 ◽  
Author(s):  
S. Appavu Alias Balamurugan ◽  
G. Athiappan ◽  
M. Muthu Pandian ◽  
R. Rajaram
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Binary Tree ◽  
Experimental Results ◽  
Classification Methods ◽  
Detection Rates ◽  
Naive Bayesian ◽  
The Past ◽  
Naïve Bayesian

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

scholarly journals Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data

2021 ◽  
Vol 10 (1) ◽  
pp. 83
Author(s):  
Solmaz Sohrabei ◽  
Alireza Atashi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Risk Factors ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Early Detection ◽  
Breast Cancers ◽  
Data Mining Techniques ◽  
Naive Bayesian ◽  
Naïve Bayesian

Introduction: Early detection breast cancer Causes it most curable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. Data mining techniques have a growing reputation in the medical field because of high predictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer.Material and Methods: The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using 20 fields (risk factor) of the database because database feature was restrictions. Used 10-fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators.Results: Naïve Bayesian and artificial neural network are better models for the prediction of breast cancer risks. Naïve Bayesian had accuracy of 93%, specificity of 93.32%, sensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, specificity of 91.98%, sensitivity of 92.69%, and ROC of 0.8.Conclusion: Strangely the different artificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy risk considers. The significant prognostic components affecting risk pace of bosom disease distinguished in this investigation, which were approved by risk, are helpful and could be converted into choice help devices in the clinical area.

Ketepatan Klasifikasi Penerima Beasiswa STMIK STIKOM Bali dengan Hybrid Self Organizing Maps dan Algoritma K-Mean

Jurnal Varian ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Ni Putu Nanik Hendayanti ◽  
Gusti Ayu Made Arna Putri ◽  
Maulida Nurhidayati
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Self Organizing Maps ◽  
Cluster Group ◽  
Artificial Neural ◽  
Clustering Data ◽  
Self Organizing

Data Mining adalah penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data yang sangat besar. Salah satu teknik yang dikenal dalam Data Mining yaitu clustering. Pengertian clustering dalam Data Mining adalah pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap di lama cluster tersebut akan berisi data yang semirip mungkin dan berbeda dengan objek dalam cluster yang lain. Salah satu metode klasifiaksi atau clustering adalah Self Organizing Maps (SOM). SOM merupakan metode artificial neural network yang digunakan untuk mengelompokkan (clustering) data berdasarkan karakteristik/fitur-fitur data. Metode pengelompokan yang menggunakan konsep jarak dan memiliki karakteristik yang hampir sama dengan SOM yaitu metode K-means. Penelitian ini bertujuan untuk mengembangkan suatu metode yang merupakan hybrid dari SOM dan K-means yang digunakan untuk menentukan ketepatan suatu klasifikasi. Sebelum diujikan pada data asli, metode hybrid SOM dan K-Means diujikan lebih dulu pada data benchmark sehingga dapat diketahui berapa persen ketepan yang dihasilkan. Kemudian dilanjutkan dengan penerapan metode hybrid SOM dan K-means pada data penerimaan beasiswa di STMIK STIKOM Bali. Penelitian ini bertujuan untuk menentukan ketepatan klasifikasi penerima beasiswa STMIK STIKOM Bali dengan metode hybrid SOM dan K-means. Hasil penelitian menunjukkan bahwa metode Kmeans dan SOM memberikan hasil yang sama yang akibatnya metode SOM-Kmeans juga memberikan hasil yang sama. Alasannya, metode SOM-Kmeans menggunakan nilai centroid dari hasil SOM, dan hasil yang diperoleh pada metode Kmean memiliki hasil yang sama dengan SOM akibatnya metode SOM-Kmeans menghasilkan hasil yang sama dengan kedua metode sebelumnya.

Implementasi Rough Neural Network dalam Identifikasi Kepuasan Konsumen Mediasi Bisnis

2018 ◽  
Vol 18 (2) ◽  
pp. 184
Author(s):  
Ikrimah Afifah Trivanni
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
New York ◽  
Artificial Neural Network ◽  
Rough Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Data mining menjadi topik hangat yang sangat bermanfaat di era saat ini. Sistem Artificial Neural Network (ANN) dan rough set yang merupakan metode data mining dapat digabungkan yang selanjutnya disebut sebagai metode Rough Neural Network (RNN). Siste, roughset dalam RNN berfungsi untuk mereduksi atribut untuk optimalisasi informasi sedangkan ANN berfungsi untuk membentuk jaringan dari kumpulan data reduksi tersebut. Metode ini dapat digunakan di berbagai bidang misalnya bisnis yakni dalam mengidentifikasi kepuasan konsumen. Perlindungan hak maupun kewajiban dalam bisnis adalah hal penting di negara maju, contohnya New York yang telah membentuk Departement of Consumen Affairs (DCA). Ribuan mediasi tercatat telah dilakukan oleh DCA New York sehingga pendekatan struktur terhadap kepuasan konsumen merupakan hal penting dalam meninjau apakah layanan mediasi yang dilakukan telah baik. Oleh karena itu, tujuan penelitian ini adalah mengimplementasikan metode RNN pada suatu dataset komplain konsumen terhadap pelayanan mediasi DCA New York. Hasil penelitian pada proses awal, rough set menunjukkan bahwa atribut yang efektif untuk menghasilkan kepuasan konsumen yang optimal adalah atribut Business State, Complaint Result, Duration of Mediation, dan Complaint Type. Eror yang dihasilkan pada jaringan tiruan kepuasan konsumen (Satisfaction) sebesar 345,828 dengan langkah yang dilalui untuk mencapai model yang mungkin adalah sebanyak 65137 langkah. Model RNN menunjukkan selisih eror yang kecil antara data latih dan data tes, artinya model RNN konsisten dalam memprediksi kepuasan konsumen untuk kedepannya.

Sign in / Sign up

Export Citation Format

Share Document