100 research outputs found
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Predicting traffic conditions has been recently explored as a way to relieve
traffic congestion. Several pioneering approaches have been proposed based on
traffic observations of the target location as well as its adjacent regions,
but they obtain somewhat limited accuracy due to lack of mining road topology.
To address the effect attenuation problem, we propose to take account of the
traffic of surrounding locations(wider than adjacent range). We propose an
end-to-end framework called DeepTransport, in which Convolutional Neural
Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain
spatial-temporal traffic information within a transport network topology. In
addition, attention mechanism is introduced to align spatial and temporal
information. Moreover, we constructed and released a real-world large traffic
condition dataset with 5-minute resolution. Our experiments on this dataset
demonstrate our method captures the complex relationship in temporal and
spatial domain. It significantly outperforms traditional statistical methods
and a state-of-the-art deep learning method
A competitive mechanism based multi-objective particle swarm optimizer with fast convergence
In the past two decades, multi-objective optimization has attracted increasing
interests in the evolutionary computation community, and a variety
of multi-objective optimization algorithms have been proposed on the
basis of different population based meta-heuristics, where the family of
multi-objective particle swarm optimization is among the most representative
ones. While the performance of most existing multi-objective particle
swarm optimization algorithms largely depends on the global or personal
best particles stored in an external archive, in this paper, we propose
a competitive mechanism based multi-objective particle swarm optimizer,
where the particles are updated on the basis of the pairwise competitions
performed in the current swarm at each generation. The performance
of the proposed competitive multi-objective particle swarm optimizer is
verified by benchmark comparisons with several state-of-the-art multiobjective
optimizers, including three multi-objective particle swarm optimization
algorithms and three multi-objective evolutionary algorithms.
Experimental results demonstrate the promising performance of the proposed
algorithm in terms of both optimization quality and convergence
speed
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search
Cognitive diagnosis plays a vital role in modern intelligent education
platforms to reveal students' proficiency in knowledge concepts for subsequent
adaptive tasks. However, due to the requirement of high model interpretability,
existing manually designed cognitive diagnosis models hold too simple
architectures to meet the demand of current intelligent education systems,
where the bias of human design also limits the emergence of effective cognitive
diagnosis models. In this paper, we propose to automatically design novel
cognitive diagnosis models by evolutionary multi-objective neural architecture
search (NAS). Specifically, we observe existing models can be represented by a
general model handling three given types of inputs and thus first design an
expressive search space for the NAS task in cognitive diagnosis. Then, we
propose multi-objective genetic programming (MOGP) to explore the NAS task's
search space by maximizing model performance and interpretability. In the MOGP
design, each architecture is transformed into a tree architecture and encoded
by a tree for easy optimization, and a tailored genetic operation based on four
sub-genetic operations is devised to generate offspring effectively. Besides,
an initialization strategy is also suggested to accelerate the convergence by
evolving half of the population from existing models' variants. Experiments on
two real-world datasets demonstrate that the cognitive diagnosis models
searched by the proposed approach exhibit significantly better performance than
existing models and also hold as good interpretability as human-designed
models.Comment: 15 pages, 12 figures, 5 table
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