Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;

Peningkatan Kemampuan Pengenalan Pola Dari Jaringan Saraf Tiruan Dengan Menggunakan Diskritisasi Chi2

Proses pelatihan backpropagation membutuhkan waktu yang cukup lama untuk mencapai tahap konvergen. Salah satu penyebabnya adalah dokumen dalam set data terdiri dari campuran antara bilangan kontinu dan diskrit. dalam hal klasifikasi, sebuah set data akan lebih mudah dibedakan dengan nilai atribut yang berbeda jauh. Metode Chi2 berhasil untuk menemukan pola dari set data sintetik yang memiliki pola data miring dan pararel. Penggabungan backpropagation dan Chi2 berhasil mempercepat proses pelatihan dan meningkatkan akurasi klasifikasi. Oleh karena itu, pengujian akan dilanjutkan dengan menggabungkan dua metode tersebut untuk mengklasifikasikan set data dari kasus nyata. Dari hasil pengujian didapatkan kesimpulan bahwa kedua metode tersebut berhasil mempercepat proses pelatihan dan meningkatkan akurasi klasifikasi

INTERPRETATION TRAINED NEURAL NETWORKS BASED ON GENETIC ALGORITHMS

ABSTRAC

Survey on the Family of the Recursive-Rule Extraction Algorithm

In this paper, we first review the theoretical and historical backgrounds on rule extraction from neural network ensembles. Because the structures of previous neural network ensembles were quite complicated, research on an efficient rule extraction algorithm from neural network ensembles has been sparse, even though a practical need exists for rule extraction in Big Data datasets. We describe the Recursive-Rule extraction (Re-RX) algorithm, which is an important step toward handling large datasets. Then we survey the family of the Recursive-Rule extraction algorithm, i.e. the Multiple-MLP Ensemble Re-RX algorithm, and present concrete applications in financial and medical domains that require extremely high accuracy for classification rules. Finally, we mention two promising ideas to considerably enhance the accuracy of the Multiple-MLP Ensemble Re-RX algorithm. We also discuss developments in the near future that will make the Multiple-MLP Ensemble Re-RX algorithm much more accurate, concise, and comprehensible rule extraction from mixed datasets

Bir Gizli Katmanlı Yapay Sinir Ağlarında Optimal Nöron Sayısının İncelenmesi

Bu makalede, bir gizli katmanlı yapay sinir ağları için optimal nöron sayısı araştırılmıştır. Bunun için teorik ve istatiksel çalışmalar yapılmıştır. Optimal nöron sayısını bulmak için global minimum bulmak gereklidir. Ancak yapay sinir ağlarının eğitimi konveks olmayan bir problem olduğundan optimizasyon algoritmaları ile global minimum bulmak zordur. Bu çalışmada global minimumu dolayısıyla optimum nöron sayısını bulmak için baskı maliyet fonksiyonu önerilmiştir. Baskı maliyet fonksiyonu yardımıyla global minimumu veren yapay sinir ağı modelinin nöron sayısının, optimal nöron sayısını verdiği gösterilmiştir. Ayrıca baskı maliyet fonksiyonu XOR veri kümesi ve daire veri kümesi üzerinde test edilmiş ve XOR veri kümesi üzerinde %99, daire veri kümesi üzerinde ise %97 başarı elde edilmiştir. Bu veri kümeleri için optimal nöron sayısı tespit edilmiştir

Redes neuronales para clasificación : una aplicación al caso de riesgos laborales en Colombia

El presente artículo describe el diseño, la formalización matemática, programación y la aplicación de una red neuronal “percepton multicapa” en un problema economía de la información. El modelo permitió clasificar correctamente el 85% de las empresas de una muestra aleatoria de asegurados a riesgos laborales en Colombia, identificándolas como fraudulentas o no fraudulentas a partir de sus variables explicativas. Este estudio cuenta con dos factores diferenciales frente a los realizados en el pasado. En primer lugar, se aplicó una red neuronal típicamente usada para modelar pronósticos de series de temporales a un problema de clasificación de individuos, siguiendo el método propuesto por Hongjun Lu, Rudy Setiono y Huan Liu en “Neuro Rule: A Connectionist Approach to Data Mining” artículo que introduce un algoritmo para generar reglas de fácil interpretación para la clasificación de individuos. En segundo lugar, la aplicación de esta técnica de minería de datos es novedosa en la detección de empresas fraudulentas afiliadas al seguro de riesgos laborales y en general en el campo de investigación económica en Colombia.The present article describes the design, mathematic formalization, programing and the application of a neural network “multilayer perceptron” in a problem of economy of the information. The model permitted classify correctly 85% of the companies from a random sample of insured to occupational hazards in Colombia, identifying them as fraudulent or non-fraudulent based on its explanatory variables. Additionally, this study has two differentiating factors towards those made in the past. First, a neural network typically used for modelling forecasts of series of temporals was applied to a problem of classification of individuals, following the method proposed by Hongjun Lu, Rudy Setiono and Huan Liu in their article “Neuro Rule: A Connectionist Approach to Data Mining” that introduces an algorithm for generate rules of easy interpretation for the classification of individuals. Second, the application of this data mining technique is novel in the detection of fraudulent companies affiliated to the insurance of occupational hazards and in the economic research field in Colombia.Magíster en EconomíaMaestrí