Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Toward the Rapid Design of Engineered Systems Through Deep Neural Networks Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Exploring the Feature Space to Aid Learning in Design Space Exploration

Authors : Hyunseung Bang, Yuan Ling Zi Shi, Guy Hoffman, So-Yeon Yoon, Daniel Selva

Published in: Design Computing and Cognition '18

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, we introduce the concept of exploring the feature space to aid learning in the context of design space exploration. The feature space is defined as a possible set of features mapped in a 2D plane with each axis representing different interestingness measures, such as precision or recall. Similar to how a designer explores the design space, one can explore the feature space by observing how different features vary in their ability to explain a set of design solutions. We hypothesize that such process helps designers gain a better understanding of the design space. To test this hypothesis, we conduct a controlled experiment with human subjects. The result suggests that exploring the feature space has the potential to enhance the user’s ability to identify important features and predict the performance of a design. However, such observation is limited only to the participants with some previous experience with design space exploration.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Utility of Evolutionary Design in Architectural Form Finding: An Investigation into Constraint Handling Strategies
next chapter Redefining Supports: Extending Mass Customization with Digital Tools for Collaborative Residential Design
Footnotes
Literature
1.
Balling R (1999) Design by shopping: a new paradigm? In: Proceedings of the third world congress of structural and multidisciplinary optimization (WCSMO-3), vol. 1, pp. 295–297
2.
Hirschi NW, Frey DD (2002) Cognition and complexity: an experiment on the effect of coupling in parameter design. Res Eng Des 13:123–131CrossRef
3.
Flager F, Gerber DJ, Kallman B (2014) Measuring the impact of scale and coupling on solution quality for building design problems. Des Stud 35(2):180–199CrossRef
4.
Grogan PT, de Weck OL (2016) Collaboration and complexity: an experiment on the effect of multi-actor coupled design. Res Eng Des 27(3):221–235CrossRef
5.
Eddy J, Lewis KE (2002) Visualization of multidimensional design and optimization using cloud visualization. In: Proceedings of DETC’02, pp 899–908
6.
Stump GM, Yukish M, Simpson TW, Harris EN (2003) Design space visualization and its application to a design by shopping paradigm. In: Proceedings of DETC’03
7.
Obayashi S, Sasaki D (2003) Visualization and data mining of Pareto solutions using self-organizing map. Evol Multi-Criterion Optim LNCS 2632:796–809
8.
(Luke) Zhang X, Simpson T, Frecker M, Lesieutre G (2012) Supporting knowledge exploration and discovery in multi-dimensional data with interactive multiscale visualisation. J Eng Des 23(1):23–47
9.
German BJ, Feigh KM, Daskilewicz M (2013) An experimental study of continuous and discrete visualization paradigms for interactive trade space exploration. J Comput Inf Sci Eng 13:1–12CrossRef
10.
Knerr N, Selva D (2016) Cityplot: visualization of high-dimensional design spaces with multiple criteria. J Mech Des 138(9):91403CrossRef
11.
Diaz MJ, Fullmer DD, Briceno SI, Mavris DN (2017) A collaborative approach to complex systems engineering using a web-based visual analytics framework. In: 55th AIAA aerospace sciences meeting
12.
Kudo F, Yoshikawa T (2012) Knowledge extraction in multi-objective optimization problem based on visualization of Pareto solutions. In: 2012 IEEE congress on evolutionary computation, CEC 2012
13.
Chen W, Chazan J, Fuge M (2016) How designs differ: non-linear embeddings illuminate intrinsic design complexity. In: Proceedings of the ASME 2016 international design engineering technical conferences and computers and information in engineering conference IDETC/CIE 2016, pp 1–12
14.
Agrawal G, Bloebaum CL, Lewis KE (2005) Intuitive design selection using visualized n-dimensional pareto frontier. In: 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, AIAA 2005–1813, pp 1–14
15.
Walker DJ, Everson RM, Fieldsend JE (2013) Visualizing mutually nondominating solution sets in many-objective optimization. IEEE Trans Evol Comput 17(2):165–184CrossRef
16.
Peng W, Ward MO, Rundensteiner EA (2004) Clutter reduction in multi-dimensional data visualization using dimension reordering. In: Proceedings of IEEE symposium on information visualization, INFO VIS, pp 89–96
17.
Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Rec 22(2):207–216CrossRef
18.
Quinlan JR (1993) C4. 5: programs for machine learning. Elsevier
19.
Cervone G, Franzese P, Keesee APK (2010) Algorithm quasi-optimal (AQ) learning. Wiley Interdiscip Rev Comput Stat 2(2):218–236CrossRef
20.
Santos JCA, Nassif H, Page D, Muggleton SH, Sternberg MJE (2012) Automated identification of protein-ligand interaction fea- tures using inductive logic programming: a hexose binding case study. BMC Bioinform 13(1):162CrossRef
21.
Bang H, Selva D (2016) iFEED: interactive feature extraction for engineering design. In: Proceedings of the ASME 2016 international design engineering technical conferences and computers and information in engineering conference IDETC/CIE 2016, pp 1–11
22.
Watanabe S, Chiba Y, Kanazaki M (2014) A proposal on analysis support system based on association rule analysis for non-dominated solutions. In: Proceedings of the 2014 IEEE congress on evolutionary computation, CEC 2014, pp 880–887
23.
Bandaru S, Ng AHC, Deb K (2017) Data mining methods for knowledge discovery in multi-objective optimization: part a—survey. Expert Syst Appl 70:139–159CrossRef
24.
Brachman R, Levesque H (2004) Knowledge representation and reasoning. Morgan Kaufmann
25.
Feldman J (2010) The simplicity principle concept learning in human. Psychol Sci 12(6):227–232
26.
Li WLW, Han JHJ, Pei JPJ (2001) CMAR: accurate and efficient classification based on multiple\nclass-association rules. In: Proceedings of the 2001 IEEE international conference data mining, pp 369–376
27.
Liu B, Hsu W, Ma Y, Ma B (1998) Integrating classification and association rule mining. In: Knowledge discovery and data mining, pp 80–86
28.
Tan P-N, Kumar V, Srivastava J (2004) Selecting the right objective measure for association analysis. Inf Syst 29(4):293–313CrossRef
29.
Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437CrossRef
30.
Buckland M, Gey F (1994) The relationship between recall and precision. J Am Soc Inf Sci 45(1):12–19CrossRef
31.
Selva D (2014) Knowledge-intensive global optimization of Earth observing system architectures: a climate-centric case study. SPIE Remote Sens 9241:92411S–92411SCrossRef
32.
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB ’94 conference on very large data bases, vol 1215, pp 487–499
Metadata
Title
Exploring the Feature Space to Aid Learning in Design Space Exploration
Authors
Hyunseung Bang
Yuan Ling Zi Shi
Guy Hoffman
So-Yeon Yoon
Daniel Selva
Copyright Year
2019
Book
Design Computing and Cognition '18

Print ISBN: 978-3-030-05362-8

Electronic ISBN: 978-3-030-05363-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-05363-5_11

Premium Partners

    Image Credits