Gürol Canbek

Dr. Gürol Canbek is a seasoned computer engineering professional with over 25 years of active experience in leading and executing numerous software projects across major organizations such as HAVELSAN, ASELSAN, Takasbank, and the Ministry of National Defense. Throughout his career, he has taken on multiple roles, demonstrating versatility and expertise in complex technological environments.

In addition to his extensive industry experience, Dr. Canbek has made significant contributions to the field of machine learning and classification. His academic research focuses on advancing and benchmarking robust performance metrics, emphasizing the critical importance of dataset quality and understanding the statistical distribution of features before feature selection and model development. Dr. Canbek has distinguished between performance measures and metrics, introduced a new category called "performance indicators," and provided a comprehensive review of binary classification performance measures. He has also developed a research and educational tool, TasKar, for calculating and visualizing these instruments. His work aims to establish a systematic approach to performance evaluation, ensuring unbiased results.

Moreover, Dr. Canbek's recent work highlights the "Garbage In, Garbage Out" (GIGO) rationale, underscoring the importance of ensuring dataset quality in artificial intelligence applications to achieve high and generalizable performance. He has proposed a technique to quantify datasets based on feature frequency distribution characteristics, providing unique insights into feature prevalence. His research in this area, demonstrated through the analysis of Android mobile malware datasets, reveals critical differences in statistical distributions, offering a method to assess dataset sufficiency before feature selection and model building. Additionally, he developed a systematic dataset profiling approach to distinguish datasets collected from different sources, identifying their strengths and weaknesses.

These contributions address two key concerns in modern AI development: performance evaluation and dataset quality/profiling.