Derya Birant

Dokuz Eylül University

Dr. Derya Birant received a BS, MS, and Ph.D. in Computer Engineering from Dokuz Eylul University, Turkey in 2000, 2002, and 2006, respectively. Since 2017, she has been an Associate Professor at the Computer Engineering Department of Dokuz Eylul University. She is also the vice-chair of the department. She was a Visiting Lecturer at South East European University in 2006 and Ege University between 2010 and 2012. Her research interests include data mining and machine learning. She is the author of six book chapters and more than seventy publications. Dr. Birant has supervised more than thirty Ph.D. and MSc students. She has been involved in more than twenty long-term interdisciplinary R&D projects on data mining.

1books edited

4chapters authored

Latest work with IntechOpen by Derya Birant

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment.

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Data Mining

Edited by Derya Birant

Chapters authored

Data Mining Using RFM Analysis

By Derya Birant

Part of the book: Knowledge-Oriented Applications in Data Mining

Service-Oriented Data Mining

By Derya Birant

Part of the book: New Fundamental Technologies in Data Mining

Ensemble Methods in Environmental Data Mining

By Goksu Tuysuzoglu, Derya Birant and Aysegul Pala

Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an individual model. The study presented in this chapter focuses on several ensemble strategies in addition to the standard single classifiers such as decision tree, naive Bayes, support vector machine, and k-nearest neighbor (KNN), popularly used in literature. This is the first study that compares four ensemble strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection (the random forest algorithm), (iii) boosting (the AdaBoost algorithm), and (iv) voting of different algorithms. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air, ecology, rainfall, and soil.

Part of the book: Data Mining

Data Mining in Banking Sector Using Weighted Decision Jungle Method

By Derya Birant

Classification, as one of the most popular data mining techniques, has been used in the banking sector for different purposes, for example, for bank customer churn prediction, credit approval, fraud detection, bank failure estimation, and bank telemarketing prediction. However, traditional classification algorithms do not take into account the class distribution, which results into undesirable performance on imbalanced banking data. To solve this problem, this paper proposes an approach which improves the decision jungle (DJ) method with a class-based weighting mechanism. The experiments conducted on 17 real-world bank datasets show that the proposed approach outperforms the decision jungle method when handling imbalanced banking data.

Part of the book: Data Mining

Derya Birant

Latest work with IntechOpen by Derya Birant

Data Mining

Chapters authored

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