dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification

• URL: http://arxiv.org/abs/2207.12165v1
• Date: Mon, 25 Jul 2022 13:04:05 GMT
• Title: dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
• Authors: Paul Boniol, Mohammed Meftah, Emmanuel Remy, Themis Palpanas
• Abstract summary: We describe a convolutional architecture that enables the comparison of dimensions. We then propose a method that returns dCAM, a Dimension-wise Class Activation Map.
• Score: 18.382700339944524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led the algorithm to some decisions is a real need in many applications. Convolutional neural networks perform well for the data series classification task; though, the explanations provided by this type of algorithm are poor for the specific case of multivariate data series. Addressing this important limitation is a significant challenge. In this paper, we propose a novel method that solves this problem by highlighting both the temporal and dimensional discriminant information. Our contribution is two-fold: we first describe a convolutional architecture that enables the comparison of dimensions; then, we propose a method that returns dCAM, a Dimension-wise Class Activation Map specifically designed for multivariate time series (and CNN-based models). Experiments with several synthetic and real datasets demonstrate that dCAM is not only more accurate than previous approaches, but the only viable solution for discriminant feature discovery and classification explanation in multivariate time series. This paper has appeared in SIGMOD'22.

Related papers

• An End-to-End Model for Time Series Classification In the Presence of Missing Values [25.129396459385873]
  Time series classification with missing data is a prevalent issue in time series analysis. This study proposes an end-to-end neural network that unifies data imputation and representation learning within a single framework.
  arXiv  Detail & Related papers  (2024-08-11T19:39:12Z)
• An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
  Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks. The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions. We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
  arXiv  Detail & Related papers  (2023-09-30T15:57:14Z)
• Evaluating Explanation Methods for Multivariate Time Series Classification [4.817429789586127]
  The main focus of this paper is on analysing and evaluating explanation methods tailored to Multivariate Time Series Classification (MTSC) We focus on saliency-based explanation methods that can point out the most relevant channels and time series points for the classification decision. We study these methods on 3 synthetic datasets and 2 real-world datasets and provide a quantitative and qualitative analysis of the explanations provided.
  arXiv  Detail & Related papers  (2023-08-29T11:24:12Z)
• DCID: Deep Canonical Information Decomposition [84.59396326810085]
  We consider the problem of identifying the signal shared between two one-dimensional target variables. We propose ICM, an evaluation metric which can be used in the presence of ground-truth labels. We also propose Deep Canonical Information Decomposition (DCID) - a simple, yet effective approach for learning the shared variables.
  arXiv  Detail & Related papers  (2023-06-27T16:59:06Z)
• CvS: Classification via Segmentation For Small Datasets [52.821178654631254]
  This paper presents CvS, a cost-effective classifier for small datasets that derives the classification labels from predicting the segmentation maps. We evaluate the effectiveness of our framework on diverse problems showing that CvS is able to achieve much higher classification results compared to previous methods when given only a handful of examples.
  arXiv  Detail & Related papers  (2021-10-29T18:41:15Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Multi-task Supervised Learning via Cross-learning [102.64082402388192]
  We consider a problem known as multi-task learning, consisting of fitting a set of regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task.
  arXiv  Detail & Related papers  (2020-10-24T21:35:57Z)
• A Method for Handling Multi-class Imbalanced Data by Geometry based Information Sampling and Class Prioritized Synthetic Data Generation (GICaPS) [15.433936272310952]
  This paper looks into the problem of handling imbalanced data in a multi-label classification problem. Two novel methods are proposed that exploit the geometric relationship between the feature vectors. The efficacy of the proposed methods is analyzed by solving a generic multi-class recognition problem.
  arXiv  Detail & Related papers  (2020-10-11T04:04:26Z)
• A Novel Random Forest Dissimilarity Measure for Multi-View Learning [8.185807285320553]
  Two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements.
  arXiv  Detail & Related papers  (2020-07-06T07:54:52Z)
• ClarQ: A large-scale and diverse dataset for Clarification Question Generation [67.1162903046619]
  We devise a novel bootstrapping framework that assists in the creation of a diverse, large-scale dataset of clarification questions based on postcomments extracted from stackexchange. We quantitatively demonstrate the utility of the newly created dataset by applying it to the downstream task of question-answering. We release this dataset in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems.
  arXiv  Detail & Related papers  (2020-06-10T17:56:50Z)
• Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations [6.6147550436077776]
  We propose new time series classification algorithms to address gaps in current approaches. Our approach is based on symbolic representations of time series, efficient sequence mining algorithms and linear classification models. Our models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series.
  arXiv  Detail & Related papers  (2020-05-31T15:32:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.