Welcome
Dr. Gürol Canbek has conducted research in the field of machine learning and classification, with a focus on benchmarking robust performance metrics, developing new performance metrics and evaluation techniques. His work also emphasizes the importance of dataset quality and understanding the statistical distribution of features before proceeding with feature selection and model building. Dr. Canbek has distinguished performance measures vs. metrics, proposed a new instrument category named "performance indicators" and has developed a comprehensive review and knowledge representation of binary classification performance measures/metrics. Additionally, he has created a research and education tool for the calculation and representation of binary classification performance instruments. Overall, Dr. Canbek's research aims to provide a systematic approach to data profiling and classification performance evaluation/publication while eliminating potential biases.
My latest publications
Springer SN Computer Science: Performance Instruments published my most recent research, which proposed a Periodic Table of Performance Instruments: "PToPI: A Comprehensive Review, Analysis, and Knowledge Representation of Binary Classification Performance Measures/Metrics" [September 2022]
2. ACCBAR (Accuracy Barrier)
In this article, I propose a new performance instrument classification category called 'indicators,' and I provide the first performance indicator instance called ACCBAR (Accuracy Barrier), which indicates problematic cases of widely used performance metrics (Accuracy) Accepted Paper [September 2023]
My research on benchmarking classification performance metrics was published in the journal Neural Computing and Applications by SpringerNature (SCI, Q1). It suggests that MCC be used to evaluate classification performance: "BenchMetrics: A systematic benchmarking method for binary-classification performance metrics" [August 2021]
An IEEE conference published my paper proposing a representation method and a compact AI tool with graphics for performance evaluation instruments. Download TasKar for free. Watch the video presentation on my YouTube channel
[December 2021]
This paper, which was presented at an IEEE conference, proposes a systematic ML process that is designed as a cycle with eight sub-processes that traverse introduced spaces (file, sample, class, feature, dataset, model, and finally metric spaces). The sub-process of dataset quality analysis/comparison is designed specifically as a quality control gateway. A case study of the Android mobile malware classification problem domain is used to explain the proposed process. [December 2021]
My article introducing a new method to compare machine-learning datasets via multiple binary-feature frequency ranks has been published in Hittite Journal of Science and Engineering. Free full-text is avilable here (click PDF in the page). [June 2021]
My paper proposing a data profiling method for datasets with big data association was published. The presentation video in Turkish is available for free. [August 2021]
My paper proposing a method for Benchmarking of Metrics for Probabilistic instruments (BenchMetrics Prob) is published [April 2023]
My paper proposing a data profiling method for datasets with big data association has been published. The presentation video in Turkish is available for free. [August 2021]
See all my publications
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