5 research outputs found
Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs
Shapley value is originally a concept in econometrics to fairly distribute
both gains and costs to players in a coalition game. In the recent decades, its
application has been extended to other areas such as marketing, engineering and
machine learning. For example, it produces reasonable solutions for problems in
sensitivity analysis, local model explanation towards the interpretable machine
learning, node importance in social network, attribution models, etc. However,
its heavy computational burden has been long recognized but rarely
investigated. Specifically, in a -player coalition game, calculating a
Shapley value requires the evaluation of or marginal contribution
values, depending on whether we are taking the permutation or combination
formulation of the Shapley value. Hence it becomes infeasible to calculate the
Shapley value when is reasonably large. A common remedy is to take a random
sample of the permutations to surrogate for the complete list of permutations.
We find an advanced sampling scheme can be designed to yield much more accurate
estimation of the Shapley value than the simple random sampling (SRS). Our
sampling scheme is based on combinatorial structures in the field of design of
experiments (DOE), particularly the order-of-addition experimental designs for
the study of how the orderings of components would affect the output. We show
that the obtained estimates are unbiased, and can sometimes deterministically
recover the original Shapley value. Both theoretical and simulations results
show that our DOE-based sampling scheme outperforms SRS in terms of estimation
accuracy. Surprisingly, it is also slightly faster than SRS. Lastly, real data
analysis is conducted for the C. elegans nervous system and the 9/11 terrorist
network
A Mutually Enhanced Bidirectional Approach for Jointly Mining User Demand and Sentiment (Student Abstract)
User demand mining aims to identify the implicit demand from the e-commerce reviews, which are always irregular, vague and diverse. Existing sentiment analysis research mainly focuses on aspect-opinion-sentiment triplet extraction, while the deeper user demands remain unexplored. In this paper, we formulate a novel research question of jointly mining aspect-opinion-sentiment-demand, and propose a Mutually Enhanced Bidirectional Extraction (MEMB) framework for capturing the dynamic interaction among different types of information. Finally, experiments on Chinese e-commerce data demonstrate the efficacy of the proposed model
An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation
Abstract Objective To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. Methods A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. Results We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. Conclusions The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. Clinical relevance statement The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment. Graphical Abstrac