Outlier Detection Ensemble with Embedded Feature Selection
- URL: http://arxiv.org/abs/2001.05492v1
- Date: Wed, 15 Jan 2020 13:14:10 GMT
- Title: Outlier Detection Ensemble with Embedded Feature Selection
- Authors: Li Cheng, Yijie Wang, Xinwang Liu, Bin Li
- Abstract summary: We propose an outlier detection ensemble framework with embedded feature selection (ODEFS)
For each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation.
We adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection.
- Score: 42.8338013000469
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Feature selection places an important role in improving the performance of
outlier detection, especially for noisy data. Existing methods usually perform
feature selection and outlier scoring separately, which would select feature
subsets that may not optimally serve for outlier detection, leading to
unsatisfying performance. In this paper, we propose an outlier detection
ensemble framework with embedded feature selection (ODEFS), to address this
issue. Specifically, for each random sub-sampling based learning component,
ODEFS unifies feature selection and outlier detection into a pairwise ranking
formulation to learn feature subsets that are tailored for the outlier
detection method. Moreover, we adopt the thresholded self-paced learning to
simultaneously optimize feature selection and example selection, which is
helpful to improve the reliability of the training set. After that, we design
an alternate algorithm with proved convergence to solve the resultant
optimization problem. In addition, we analyze the generalization error bound of
the proposed framework, which provides theoretical guarantee on the method and
insightful practical guidance. Comprehensive experimental results on 12
real-world datasets from diverse domains validate the superiority of the
proposed ODEFS.
Related papers
- Feature Selection as Deep Sequential Generative Learning [50.00973409680637]
We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses.
Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores.
arXiv Detail & Related papers (2024-03-06T16:31:56Z) - A Contrast Based Feature Selection Algorithm for High-dimensional Data
set in Machine Learning [9.596923373834093]
We propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes.
We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation.
arXiv Detail & Related papers (2024-01-15T05:32:35Z) - Robust Outlier Rejection for 3D Registration with Variational Bayes [70.98659381852787]
We develop a novel variational non-local network-based outlier rejection framework for robust alignment.
We propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation.
arXiv Detail & Related papers (2023-04-04T03:48:56Z) - Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor
Problem [8.281391209717105]
We study the feature-based news vendor problem, in which a decision-maker has access to historical data.
In this setting, we investigate feature selection, aiming to derive sparse, explainable models with improved out-of-sample performance.
We present a mixed integer linear program reformulation for the bilevel program, which can be solved to optimality with standard optimization solvers.
arXiv Detail & Related papers (2022-09-12T08:52:26Z) - Meta-Wrapper: Differentiable Wrapping Operator for User Interest
Selection in CTR Prediction [97.99938802797377]
Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems.
Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success.
We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
arXiv Detail & Related papers (2022-06-28T03:28:15Z) - Compactness Score: A Fast Filter Method for Unsupervised Feature
Selection [66.84571085643928]
We propose a fast unsupervised feature selection method, named as, Compactness Score (CSUFS) to select desired features.
Our proposed algorithm seems to be more accurate and efficient compared with existing algorithms.
arXiv Detail & Related papers (2022-01-31T13:01:37Z) - Dynamic Instance-Wise Classification in Correlated Feature Spaces [15.351282873821935]
In a typical machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training.
A new method is proposed that sequentially selects the best feature to evaluate for each test instance individually, and stops the selection process to make a prediction once it determines that no further improvement can be achieved with respect to classification accuracy.
The effectiveness, generalizability, and scalability of the proposed method is illustrated on a variety of real-world datasets from diverse application domains.
arXiv Detail & Related papers (2021-06-08T20:20:36Z) - Joint Adaptive Graph and Structured Sparsity Regularization for
Unsupervised Feature Selection [6.41804410246642]
We propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method.
A subset of optimal features will be selected in group, and the number of selected features will be determined automatically.
Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method.
arXiv Detail & Related papers (2020-10-09T08:17:04Z) - Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
We consider fairness in the integration component of data management.
We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
arXiv Detail & Related papers (2020-06-10T20:20:10Z)
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.