An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach

Okesola, Olatunji; Okokpujie, Kennedy; Adewale, Adeyinka A.; John, Samuel; Omoruyi, Osemwegie

doi:10.1109/csci.2017.36

Cited by 18 publications

(8 citation statements)

References 19 publications

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“…Managing the existing knowledge flow in tertiary institutions is essential. According to Kayıkçı and Ozan 1 , knowledge is a powerful tool for organisational competition and therefore becomes significant to every industry including banking, education and governmental sectors [2][3][4][5] . Knowledge generated should be properly managed to ensure its future availability.…”

Section: V1mentioning

confidence: 99%

Knowledge management awareness assessment in Nigerian tertiary institutions

Ogunbanwo¹,

Okesola

Buckley³

2019

F1000Res

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Background: Knowledge management (KM) is a recipe for increasing performance and promoting innovation in tertiary institutions. However, some scholars argue that the Nigerian educational sector is yet to fully appreciate the importance of KM as their KM awareness level is still low. Since measurement is the basic foundation to accomplish success, this paper assesses the KM awareness level in tertiary institutions of south-west Nigeria. Methods: We applied a survey method using a closed ended questionnaire administered to 50 participants from each of the 10 institutions measured by Likert scaling. Employing SPSS for data analysis, frequency count and percentage score were adopted to analyse the demographic data, and the research hypotheses were analysed with chi square test, Pearson chi square and bivariate correlation (Pearson) analysis. Results: A positive relationship between awareness, current status and level of familiarity was noted. KM awareness level in the institutions is high even though there is a significant difference between the public and private universities, as well as between the students and academic staff. Conclusions: Since an increase in the awareness level increases both current status and level of familiarity which often account for KM success, it is recommend that KM awareness level should continuously be improved upon in Nigerian tertiary institutions.

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Section: V1mentioning

confidence: 99%

Knowledge management awareness assessment in Nigerian tertiary institutions

Ogunbanwo¹,

Okesola

Buckley³

2019

F1000Res

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“…It uses the raw text document for training purposes, and the classifier uses the vectorized training data supplied by the vectorizer [17]. Naive Bayes vectorization focuses on the Bayes formula with presumed independence among predictors, using a set of training data to calculate the posterior probability, which is calculating the likelihood and estimates the probability terms needed for classification [18] [19]. In the context of document classification, the Naive Bayes vectorization uses the probability of a particular document being annotated to a particular category, given that the document contains certain words in it, is equal to the probability of finding those particular words in that category, times the probability that any document is annotated to that category, divided by the probability of finding those words in any document [20][21], as shown in equation ( 1):…”

Section: Naïve Bayes Vectorizationmentioning

confidence: 99%

Multi-class Document Classification using Support Vector Machine (SVM) Based on Improved Naïve Bayes Vectorization Technique

Sueno¹

2020

IJATCSE

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At present several vectorization approaches are used to transform text documents into a numerical format. A huge number of features converted from text data from a single document take time to process vectorized data with large dimensions. To reduce the number of dimensions, this work uses an improved Naïve Bayes algorithm to vectorize documents according to a distribution of probabilities reflecting the probable categories to which the document that belongs. The improved Naïve Bayes vectorization used Laplace smoothing to ensure that posterior probabilities are never zero and logarithmic function to solve the result of the probability calculation that is too small that cannot be represented. The text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM), use this probability distribution as the vectors to represent the document that is used to classify the documents. To validate the improvement of the Naïve Bayes vectorization technique, the results are compared to TF-IDF vectorization. The results showed that the transformation of data by improved Naïve Bayes vectorization technique reduces dimensionality and has contributed to better performance of the SVM classification approach.

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“…Algoritma decision tree juga memiliki nilai akurasi yang baik pada penelitian loyalitas pelanggan [3]. Penggunaan decision tree pada penelitian peningkatan kredit bank [4] juga telah dilakukan dengan tingkat akurasi mencapai 82%. Penelitan dengan logistic regression pada dataset resiko kartu kredit [5] menunjukkan bahwa nilai akurasinya mencapai 74% dan 70% pada algoritma Decision Tree.…”

Section: Pendahuluanunclassified

Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online

Rahmawati

Agustina

2020

J-TIT

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Abstract—The rapid growth of online shopping sites makes business in the virtual world very promising. Purchasing intentions is one of the keys to success in an online store. There are several data mining methods for making predictions on online purchase intentions datasets. Data can represent the characteristics or habits of each user who has visited a site whether it ends with a transaction or not. Some popular algorithms with good performance in data mining include J48 and Logistic Regression. However, in data sometimes there is a problem of class imbalance, so the ensemble technique needs to be applied. One technique that can be applied is bagging. This research examines data using bagging techniques to improve the performance of the J48 algorithm and Logistic Regression. The results of improving the performance of data mining algorithms with these techniques have an accuracy value of 89.68% for the J48 algorithm and 88.50% for the Logistic Regression algorithm. This figure shows an increase when compared with initial testing without using ensemble techniques. Increases were also experienced in Recall, F-Measure, and AUC values. Keywords—purchasing intentions; J48; Logistic Regression; Bagging; Abstrak— Pesatnya situs pembelanjaan online menjadikan bisnis di dunia virtual sangat menjanjikan. Minat pembelian menjadi salah satu kunci kesuksesan pada sebuah toko online. Terdapat beberapa metode data mining untuk melakukan prediksi pada dataset minat pembelian online. Data dapat mewakili karakteristik atau kebiasaan dari setiap user yang telah mengunjungi suatu situs baik berakhir dengan melakukan transaksi ataupun tidak. Beberapa algoritma yang populer dengan kinerja yang baik dalam data mining diantaranya J48 dan Logistic Regreession. Namun, dalam sebuah data terkadang terdapat masalah ketidakseimbangan kelas, sehingga perlu diterapkan teknik ensemble. Salah satu teknik yang dapat diterapkan adalah teknik bagging. Penelitian kali ini mengujikan data dengan teknik bagging untuk meningkatkan kinerja algoritma J48 dan Logistic Regression. Hasil dari peningkatan kinerja algoritma data mining dengan teknik tersebut memiliki nilai akurasi 89.68% untuk algoritma J48 dan 88.50% untuk algoritma Logistic Regression. Angka tersebut menunjukan adanya peningkatan jika dibandingkan dengan pengujian awal tanpa menggunakan teknik ensemble. Peningkatan juga dialami pada nilai Recall, F-Measure, dan AUC. Keywords—Minat Pembelian, J48, Logistic Regression, Bagging

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An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach

Cited by 18 publications

References 19 publications

Knowledge management awareness assessment in Nigerian tertiary institutions

Knowledge management awareness assessment in Nigerian tertiary institutions

Multi-class Document Classification using Support Vector Machine (SVM) Based on Improved Naïve Bayes Vectorization Technique

Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online

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