790 research outputs found
Data-driven multinomial random forest
In this article, we strengthen the proof methods of some previously weakly
consistent variants of random forests into strongly consistent proof methods,
and improve the data utilization of these variants, in order to obtain better
theoretical properties and experimental performance. In addition, based on the
multinomial random forest (MRF) and Bernoulli random forest (BRF), we propose a
data-driven multinomial random forest (DMRF) algorithm, which has lower
complexity than MRF and higher complexity than BRF while satisfying strong
consistency. It has better performance in classification and regression
problems than previous RF variants that only satisfy weak consistency, and in
most cases even surpasses standard random forest. To the best of our knowledge,
DMRF is currently the most excellent strongly consistent RF variant with low
algorithm complexityComment: arXiv admin note: substantial text overlap with arXiv:2211.1515
Dynamic Portfolio Management with Reinforcement Learning
Dynamic Portfolio Management is a domain that concerns the continuous
redistribution of assets within a portfolio to maximize the total return in a
given period of time. With the recent advancement in machine learning and
artificial intelligence, many efforts have been put in designing and
discovering efficient algorithmic ways to manage the portfolio. This paper
presents two different reinforcement learning agents, policy gradient
actor-critic and evolution strategy. The performance of the two agents is
compared during backtesting. We also discuss the problem set up from state
space design, to state value function approximator and policy control design.
We include the short position to give the agent more flexibility during assets
redistribution and a constant trading cost of 0.25%. The agent is able to
achieve 5% return in 10 days daily trading despite 0.25% trading cost
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification
Resting-state functional magnetic resonance imaging (rs-fMRI) offers a
non-invasive approach to examining abnormal brain connectivity associated with
brain disorders. Graph neural network (GNN) gains popularity in fMRI
representation learning and brain disorder analysis with powerful graph
representation capabilities. Training a general GNN often necessitates a
large-scale dataset from multiple imaging centers/sites, but centralizing
multi-site data generally faces inherent challenges related to data privacy,
security, and storage burden. Federated Learning (FL) enables collaborative
model training without centralized multi-site fMRI data. Unfortunately,
previous FL approaches for fMRI analysis often ignore site-specificity,
including demographic factors such as age, gender, and education level. To this
end, we propose a specificity-aware federated graph learning (SFGL) framework
for rs-fMRI analysis and automated brain disorder identification, with a server
and multiple clients/sites for federated model aggregation and prediction. At
each client, our model consists of a shared and a personalized branch, where
parameters of the shared branch are sent to the server while those of the
personalized branch remain local. This can facilitate knowledge sharing among
sites and also helps preserve site specificity. In the shared branch, we employ
a spatio-temporal attention graph isomorphism network to learn dynamic fMRI
representations. In the personalized branch, we integrate vectorized
demographic information (i.e., age, gender, and education years) and functional
connectivity networks to preserve site-specific characteristics.
Representations generated by the two branches are then fused for
classification. Experimental results on two fMRI datasets with a total of 1,218
subjects suggest that SFGL outperforms several state-of-the-art approaches
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