38 citations · 44 across the 15 of their papers we have counts for
6 papers · 1 filter
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…