28,264 research outputs found
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
Robust and cost-effective approach for discovering action rules
The main goal of Knowledge Discovery in
Databases is to find interesting and usable patterns, meaningful
in their domain. Actionable Knowledge Discovery came to
existence as a direct respond to the need of finding more usable
patterns called actionable patterns. Traditional data mining
and algorithms are often confined to deliver frequent patterns
and come short for suggesting how to make these patterns
actionable. In this scenario the users are expected to act.
However, the users are not advised about what to do with
delivered patterns in order to make them usable. In this paper,
we present an automated approach to focus on not only creating
rules but also making the discovered rules actionable.
Up to now few works have been reported in this field which
lacking incomprehensibility to the user, overlooking the cost
and not providing rule generality. Here we attempt to present a
method to resolving these issues. In this paper CEARDM
method is proposed to discover cost-effective action rules from
data. These rules offer some cost-effective changes to
transferring low profitable instances to higher profitable ones.
We also propose an idea for improving in CEARDM method
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
TinkerCell: Modular CAD Tool for Synthetic Biology
Synthetic biology brings together concepts and techniques from engineering
and biology. In this field, computer-aided design (CAD) is necessary in order
to bridge the gap between computational modeling and biological data. An
application named TinkerCell has been created in order to serve as a CAD tool
for synthetic biology. TinkerCell is a visual modeling tool that supports a
hierarchy of biological parts. Each part in this hierarchy consists of a set of
attributes that define the part, such as sequence or rate constants. Models
that are constructed using these parts can be analyzed using various C and
Python programs that are hosted by TinkerCell via an extensive C and Python
API. TinkerCell supports the notion of a module, which are networks with
interfaces. Such modules can be connected to each other, forming larger modular
networks. Because TinkerCell associates parameters and equations in a model
with their respective part, parts can be loaded from databases along with their
parameters and rate equations. The modular network design can be used to
exchange modules as well as test the concept of modularity in biological
systems. The flexible modeling framework along with the C and Python API allows
TinkerCell to serve as a host to numerous third-party algorithms. TinkerCell is
a free and open-source project under the Berkeley Software Distribution
license. Downloads, documentation, and tutorials are available at
www.tinkercell.com.Comment: 23 pages, 20 figure
Structure Selection of Polynomial NARX Models using Two Dimensional (2D) Particle Swarms
The present study applies a novel two-dimensional learning framework
(2D-UPSO) based on particle swarms for structure selection of polynomial
nonlinear auto-regressive with exogenous inputs (NARX) models. This learning
approach explicitly incorporates the information about the cardinality (i.e.,
the number of terms) into the structure selection process. Initially, the
effectiveness of the proposed approach was compared against the classical
genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO
is superior. Further, since the performance of any meta-heuristic search
algorithm is critically dependent on the choice of the fitness function, the
efficacy of the proposed approach was investigated using two distinct
information theoretic criteria such as Akaike and Bayesian information
criterion. The robustness of this approach against various levels of
measurement noise is also studied. Simulation results on various nonlinear
systems demonstrate that the proposed algorithm could accurately determine the
structure of the polynomial NARX model even under the influence of measurement
noise
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