44,435 research outputs found
Automating biomedical data science through tree-based pipeline optimization
Over the past decade, data science and machine learning has grown from a
mysterious art form to a staple tool across a variety of fields in academia,
business, and government. In this paper, we introduce the concept of tree-based
pipeline optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement a Tree-based Pipeline Optimization
Tool (TPOT) and demonstrate its effectiveness on a series of simulated and
real-world genetic data sets. In particular, we show that TPOT can build
machine learning pipelines that achieve competitive classification accuracy and
discover novel pipeline operators---such as synthetic feature
constructors---that significantly improve classification accuracy on these data
sets. We also highlight the current challenges to pipeline optimization, such
as the tendency to produce pipelines that overfit the data, and suggest future
research paths to overcome these challenges. As such, this work represents an
early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement an open source Tree-based Pipeline
Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a
series of simulated and real-world benchmark data sets. In particular, we show
that TPOT can design machine learning pipelines that provide a significant
improvement over a basic machine learning analysis while requiring little to no
input nor prior knowledge from the user. We also address the tendency for TPOT
to design overly complex pipelines by integrating Pareto optimization, which
produces compact pipelines without sacrificing classification accuracy. As
such, this work represents an important step toward fully automating machine
learning pipeline design.Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet
made from reviewer comment
Towards Automating the Construction & Maintenance of Attack Trees: a Feasibility Study
Security risk management can be applied on well-defined or existing systems;
in this case, the objective is to identify existing vulnerabilities, assess the
risks and provide for the adequate countermeasures. Security risk management
can also be applied very early in the system's development life-cycle, when its
architecture is still poorly defined; in this case, the objective is to
positively influence the design work so as to produce a secure architecture
from the start. The latter work is made difficult by the uncertainties on the
architecture and the multiple round-trips required to keep the risk assessment
study and the system architecture aligned. This is particularly true for very
large projects running over many years. This paper addresses the issues raised
by those risk assessment studies performed early in the system's development
life-cycle. Based on industrial experience, it asserts that attack trees can
help solve the human cognitive scalability issue related to securing those
large, continuously-changing system-designs. However, big attack trees are
difficult to build, and even more difficult to maintain. This paper therefore
proposes a systematic approach to automate the construction and maintenance of
such big attack trees, based on the system's operational and logical
architectures, the system's traditional risk assessment study and a security
knowledge database.Comment: In Proceedings GraMSec 2014, arXiv:1404.163
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Automating the Composition of Middleware Configurations
A method is presented for the automatic construction of all possible valid compositions of different middleware software architectures. This allows reusing the latter in order to create systems providing a set of different non-functional properties. These compositions are constructed by using only the structural information of the architectures, i.e. their configurations. Yet, they provide a valuable insight on the different properties of the class of systems that can be constructed when a particular set of non-functional properties is required
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