Journal
Scientific and Technical Journal of Information Technologies, Mechanics and Optics
UDK
Issue:5 (135)
Download PDF0 Kbyte
A meta-feature selection method based on the Auto-sklearn framework
Annotation
In recent years, the task of selecting and tuning machine learning algorithms has been increasingly solved using automated frameworks. This is motivated by the fact that when dealing with large amounts of data, classical methods are not efficient in terms of time and quality. This paper discusses the Auto-sklearn framework as one of the best solutions for automated selection and tuning machine learning algorithms. The problem of Auto-sklearn 1.0 solution based on Bayesian optimization and meta-learning is investigated. A solution to this problem is presented. A new method of operation based on meta-database optimization is proposed. The essence of the method is to use the BIRCH clustering algorithm to separate datasets into different groups. The selection criteria are the silhouette measure and the minimum number of initial Bayesian optimization configurations. The next step uses a random forest model, which is trained on a set of meta-features and the resulting labels. Important meta-features are selected from the entire set. As a result, an optimal set of important meta-features is obtained, which is used to find the initial Bayesian optimization configurations. The described method significantly speeds up the search for the best machine learning algorithm for classification tasks. The experiments were conducted with datasets from OpenML to compare Auto-sklearn 1.0, 2.0 and a new version that uses the proposed method. According to the results of the experiment and statistical Wilcoxon T-criterion tests, the new method was able to outperform the original versions in terms of time, outperforms Auto-sklearn 1.0 and competes with Auto-sklearn 2.0. The proposed method will help to speed up the time to find the best solution for machine learning tasks. Optimization of such frameworks is reasonable in terms of saving time and other resources, especially when working with large amounts of data.
Keywords
Постоянный URL
Articles in current issue
- Features of images of water, ice, snow, objects and a human formed by a hybrid television camera in the near-infrared range
- Analyzing periodical textured silicon solar cells by the TCAD modeling
- Scintillation gamma radiation sensors based on solid-state photomultipliers in wireless industrial internet networks
- Improving the quality of network management of technological processes
- Geometric approach to the solution of the Dubins car problem in the formation of program trajectories
- Drift of two-dimensional vacancy islands on the Si(100) surface under electromigration conditions
- A study of the photocatalytic properties of chitosan-TiO2 composites for pyrene decomposition
- Kinetics of transformation of the atomic step bunches shape under electromigration conditions on the Si(001) surface
- Abnormal diffusion profile of adatoms on extremely wide terraces of the Si(111) surface
- An experimental methodology for assessing the probability and danger of network attacks in automated systems
- A meta-feature selection method based on the Auto-sklearn framework
- Automatic construction of the dialog tree based on unmarked text corpora in Russian
- Generic programming with combinators and objects
- Machine learning of the Bayesian belief network as a tool for evaluating the process frequency on social network data
- Software restructuring models for object oriented programming languages using the fuzzy based clustering algorithm
- The concept of managing the network structure of intelligent devices in the digital transformation of the energy industry
- Protecting facial images from recognition on social media: solution methods and their perspective
- Redundant models of testable distributed real-time computing systems
- A study of the influence of the base thickness on photoelectric parameters of silicon solar cells with the new TCAD algorithms
- A balanced algorithm of the hybrid large-particle method and its verification on some test problems
- The architecture of a system for full-text search by speech data based on a global search index
- Assessment of cerebral circulation through an intact skull using imaging photoplethysmography