Automatic composition and optimisation of multicomponent predictive system.

Salvador, M. M., Budka, M. and Gabrys, B., 2016. Automatic composition and optimisation of multicomponent predictive system. IEEE Transactions on Knowledge and Data Engineering. (In Press)

This is the latest version of this eprint.

Full text available as:

[img] PDF
IEEE_TNNLS_SalvadorBudkaGabrys_Automatic_composition_and_optimisation_of_multicomponent_predictive_systems.pdf - Accepted Version
Restricted to Repository staff only
Available under License Creative Commons Attribution Non-commercial No Derivatives.

745kB

Abstract

Composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. This paper is concerned with theoretical considerations and extensive experimental analysis for automating the task of building such predictive systems. In the theoretical part of the paper, we first propose to adopt the Well-handled and Acyclic Workflow (WA-WF) Petri net as a formal representation of MCPSs. We then define the optimisation problem in which the search space consists of suitably parametrised directed acyclic graphs (i.e. WA-WFs) forming the sought MCPS solutions. In the experimental analysis we focus on examining the impact of considerably extending the search space resulting from incorporating multiple sequential data cleaning and preprocessing steps in the process of composing optimised MCPSs, and the quality of the solutions found. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs for 21 publicly available datasets and 7 datasets from real chemical processes. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. Our findings can have a major impact on development of high quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.

Item Type:Article
ISSN:1558-2191
Additional Information:(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
Uncontrolled Keywords:Automatic predictive model building and parametrisation; Multicomponent predictive systems; KDD process; CASH problem; Bayesian optimisation; Data preprocessing; Predictive modelling; Petri nets
Subjects:UNSPECIFIED
Group:Faculty of Science & Technology
ID Code:24673
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:26 Sep 2016 11:56
Last Modified:26 Sep 2016 11:56

Available Versions of this Item

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -