47,394 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
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
Designing the structure of neural networks is considered one of the most
challenging tasks in deep learning, especially when there is few prior
knowledge about the task domain. In this paper, we propose an
Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of
succession, extinction, mimicry, and gene duplication to search neural network
structure from scratch with poorly initialized simple network and few
constraints forced during the evolution, as we assume no prior knowledge about
the task domain. Specifically, we first use primary succession to rapidly
evolve a population of poorly initialized neural network structures into a more
diverse population, followed by a secondary succession stage for fine-grained
searching based on the networks from the primary succession. Extinction is
applied in both stages to reduce computational cost. Mimicry is employed during
the entire evolution process to help the inferior networks imitate the behavior
of a superior network and gene duplication is utilized to duplicate the learned
blocks of novel structures, both of which help to find better network
structures. Experimental results show that our proposed approach can achieve
similar or better performance compared to the existing genetic approaches with
dramatically reduced computation cost. For example, the network discovered by
our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU
hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].Comment: CVPR 201
Learning Effective Changes for Software Projects
The primary motivation of much of software analytics is decision making. How
to make these decisions? Should one make decisions based on lessons that arise
from within a particular project? Or should one generate these decisions from
across multiple projects? This work is an attempt to answer these questions.
Our work was motivated by a realization that much of the current generation
software analytics tools focus primarily on prediction. Indeed prediction is a
useful task, but it is usually followed by "planning" about what actions need
to be taken. This research seeks to address the planning task by seeking
methods that support actionable analytics that offer clear guidance on what to
do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set
of actionable plans within and across projects. Each of these plans, if
followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu
Evolutionary model type selection for global surrogate modeling
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type
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