3 citations · 3 across the 9 of their papers we have counts for
9 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…
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…
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…
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…