[1]XIANG Xu,YU Hong,ZHANG Xiaoxia,et al.IsomapVSG-LIME: a novel local interpretable model-agnostic explanations[J].CAAI Transactions on Intelligent Systems,2023,18(4):841-848.[doi:10.11992/tis.202209010]
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IsomapVSG-LIME: a novel local interpretable model-agnostic explanations

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 841-848 Column: 人工智能院长论坛 Public date: 2023-07-15
Title:
IsomapVSG-LIME: a novel local interpretable model-agnostic explanations
Author(s):
XIANG Xu;  YU Hong;  ZHANG Xiaoxia;  WANG Guoyin
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Keywords:
local interpretable model-agnostic explanations(LIME); machine learning; IsomapVSG; hierarchical agglomerative clustering; stability; local fidelity; random perturbation sampling; features sequence stability index(FSSI)
CLC:
TP181
DOI:
10.11992/tis.202209010
Abstract:
In order to solve the problem of lacking local fidelity and stability caused by local interpretable model-agnostic explanations (LIME) random perturbation sampling method, a new local interpretable model-agnostic explanation, IsomapVSG-LIME is proposed in this paper. In this method, isometric mapping virtual sample generation (IsomapVSG), a virtual sample generation method based on manifold learning, is used in substitution of random perturbation sampling method of LIME to generate samples, and aggregation hierarchical clustering method is used to select representative samples from virtual samples for training explanation model. In addition, this paper also proposes a new explanation stability evaluation index, the features sequence stability index (FSSI), which solves the problem that previous evaluation indexes ignore the sequential relationship of features and the flipping of explanations. Experimental results show that the proposed method outperforms the latest models in terms of stability and local fidelity.
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