38 citations · 44 across the 15 of their papers we have counts for
15 papers
Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version
Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluation techniques to measure the…
An Empirical Study of Feature Selection Granularity
Muhammad Rajabinasab, Arthur Zimek
Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution bette…
Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection
Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly +1
Many novel unsupervised feature selection methods are proposed each year, yet their empirical evaluation is limited to supervised and unsupervised evaluation metrics computed on se…
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
Pernille Matthews, Lena Krieger, Tommaso Amico +3
Clustering is an unsupervised technique for grouping data points by similarity. While explainability methods exist for supervised machine learning, they are not directly applicable…
MARS: Magnitude-Aware Rank Statistics
Muhammad Rajabinasab, Afsaneh M. Nejad, Arthur Zimek
Comprehensive evaluation of machine learning models is the key to make sure that they perform as robustly and consistently as desired. In order to summarize the experimental result…
FSEVAL: Feature Selection Evaluation Toolbox and Dashboard
Muhammad Rajabinasab, Arthur Zimek
Feature selection is a fundamental machine learning and data mining task, involved with discriminating redundant features from informative ones. It is an attempt to address the cur…
DCFO: Density-Based Counterfactuals for Outliers -- Additional Material
Tommaso Amico, Pernille Matthews, Lena Krieger +2
Outlier detection identifies data points that significantly deviate from the majority of the data distribution. Explaining outliers is crucial for understanding the underlying fact…
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Tobias Hyrup, Emmanouil Panagiotou, Arjun Roy +3
As privacy regulations such as the GDPR and HIPAA and responsibility frameworks for artificial intelligence such as the AI Act gain traction, the ethical and responsible use of rea…
Randomized PCA Forest for Unsupervised Outlier Detection
Muhammad Rajabinasab, Farhad Pakdaman, Moncef Gabbouj +2
We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in app…
Disjoint Generation of Synthetic Data
Anton Danholt Lautrup, Muhammad Rajabinasab, Tobias Hyrup +2
We propose a new framework for generating tabular synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into disjoint subsets that are suppl…
Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version
Muhammad Rajabinasab, Anton D. Lautrup, Arthur Zimek
Measuring inter-dataset similarity is an important task in machine learning and data mining with various use cases and applications. Existing methods for measuring inter-dataset si…
Transparent Neighborhood Approximation for Text Classifier Explanation
Yi Cai, Arthur Zimek, Eirini Ntoutsi +1
Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to impr…
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)
Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello +2
Outlier detection algorithms typically assign an outlier score to each observation in a dataset, indicating the degree to which an observation is an outlier. However, these scores…
FSDEM: Feature Selection Dynamic Evaluation Metric
Muhammad Rajabinasab, Anton D. Lautrup, Tobias Hyrup +1
Expressive evaluation metrics are indispensable for informative experiments in all areas, and while several metrics are established in some areas, in others, such as feature select…
SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek +1
With the growing demand for synthetic data to address contemporary issues in machine learning, such as data scarcity, data fairness, and data privacy, having robust tools for asses…