2 research outputs found
Supervised Infinite Feature Selection
In this paper, we present a new feature selection method that is suitable for
both unsupervised and supervised problems. We build upon the recently proposed
Infinite Feature Selection (IFS) method where feature subsets of all sizes
(including infinity) are considered. We extend IFS in two ways. First, we
propose a supervised version of it. Second, we propose new ways of forming the
feature adjacency matrix that perform better for unsupervised problems. We
extensively evaluate our methods on many benchmark datasets, including large
image-classification datasets (PASCAL VOC), and show that our methods
outperform both the IFS and the widely used "minimum-redundancy
maximum-relevancy (mRMR)" feature selection algorithm
Benchmark and Survey of Automated Machine Learning Frameworks
Machine learning (ML) has become a vital part in many aspects of our daily
life. However, building well performing machine learning applications requires
highly specialized data scientists and domain experts. Automated machine
learning (AutoML) aims to reduce the demand for data scientists by enabling
domain experts to build machine learning applications automatically without
extensive knowledge of statistics and machine learning. This paper is a
combination of a survey on current AutoML methods and a benchmark of popular
AutoML frameworks on real data sets. Driven by the selected frameworks for
evaluation, we summarize and review important AutoML techniques and methods
concerning every step in building an ML pipeline. The selected AutoML
frameworks are evaluated on 137 data sets from established AutoML benchmark
suits.Comment: Revised version accepted for publication at Journal of Artificial
Intelligence Research (JAIR