SemanticMask: A Contrastive View Design for Anomaly Detection in Tabular Data
Authors: Shuting Tao, Tongtian Zhu, Hongwei Wang, Xiangming Meng
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiment results validate the superiority of Semantic Mask over the state-of-the-art anomaly detection methods and existing augmentation techniques for tabular data. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Zhejiang University 2The Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University |
| Pseudocode | No | The paper includes a block diagram (Figure 1) and mathematical formulations but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and appendix are available on Git Hub at https://github.com/TST826/Semantic Mask. |
| Open Datasets | Yes | We conduct experiments on nine datasets with column names sourced from the Outlier Detection Data Sets (ODDS) [Rayana, 2016], the KEEL datasets [Derrac et al., 2015] and the UCI datasets [Markelle et al., 2013]. |
| Dataset Splits | Yes | We train our method on a random selected 50% subset of the normal data. The validation set, consisting of 25% normal data, is used to determine the threshold. The methods are then tested on the remaining normal data and all anomalous samples. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for conducting the experiments. |
| Software Dependencies | No | The paper mentions software components like Sentence-BERT and Adam optimizer, but does not provide specific version numbers for any libraries, frameworks, or programming languages used in the implementation or experimentation. |
| Experiment Setup | Yes | For Semantic Mask and its variants, λ is set to 0.5, pm is selected from the set {0.4, 0.5, 0.6}. For Semantic Mask+description, ϵ is set to 0.1. We set k of k-means proportionally to the feature dimension d. For d < 18, k = 2. For 18 d < 100, k = 3. For complex datasets such as Arrhythmia [Rayana, 2016], where d ≥ 100, k = d/100 + 3, features are partitioned into k clusters, forming two disjoint subsets with k/2 clusters each. Contrastive loss uses a constant temperature τ of 0.01. The threshold for identifying anomalies is determined by the 85th quantiles of the Mahalanobis distance in the validation set. The encoder is a multilayer perceptron consisting of two hidden layers with 128 and 64 hidden units, along with the Re LU activation layer. The encoder is trained using the Adam optimizer with a learning rate of 0.001 and default values for other hyperparameters. |