# LSCP: Locally Selective Combination in Parallel Outlier Ensembles

arXiv: https://arxiv.org/abs/1812.01528 · HTML: https://mlsanity.com/paper/1812.01528/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 30 of 32)

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- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
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- [TOD: GPU-accelerated Outlier Detection via Tensor Operations](https://mlsanity.com/paper/2110.14007/) (2021)
- [Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls](https://mlsanity.com/paper/2501.02975/) (2025)
- [Comparison of Outlier Detection Techniques for Structured Data](https://mlsanity.com/paper/2106.08779/) (2021)
- [NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction](https://mlsanity.com/paper/2203.08339/) (2022)
- [Negative Selection Algorithm Research and Applications in the last decade: A Review](https://mlsanity.com/paper/2105.06109/) (2021)
- [Outlier Detection using AI: A Survey](https://mlsanity.com/paper/2112.00588/) (2021)
- [Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model](https://mlsanity.com/paper/2204.07014/) (2022)
- [Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets](https://mlsanity.com/paper/2211.13681/) (2022)
- [Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning](https://mlsanity.com/paper/2309.15130/) (2023)
- [A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text](https://mlsanity.com/paper/2405.13031/) (2024)
- [Towards Unsupervised Validation of Anomaly-Detection Models](https://mlsanity.com/paper/2410.14579/) (2024)
- [ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection](https://mlsanity.com/paper/2607.04894/) (2026)
- [BikNN: Anomaly Estimation in Bilateral Domains with k-Nearest Neighbors](https://mlsanity.com/paper/2105.05037/) (2021)
- [IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation](https://mlsanity.com/paper/2108.00360/) (2021)
- [Unsupervised Abnormal Traffic Detection through Topological Flow Analysis](https://mlsanity.com/paper/2205.07109/) (2022)
- [Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System](https://mlsanity.com/paper/2305.00982/) (2023)
- [fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library](https://mlsanity.com/paper/2406.05999/) (2024)
- [Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2501.14694/) (2025)
- [RFOD: Random Forest-based Outlier Detection for Tabular Data](https://mlsanity.com/paper/2510.08747/) (2025)
- [DiGAN: Diffusion-Guided Attention Network for Early Alzheimer's Disease Detection](https://mlsanity.com/paper/2602.03881/) (2026)
- [Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version](https://mlsanity.com/paper/2605.16567/) (2026)
- [INCLAIR: Inception-Based Longitudinal Clinical Anomaly Detection with Informed Reasoning](https://mlsanity.com/paper/2607.27487/) (2026)
- [Witnesses Explain Anomalies](https://mlsanity.com/paper/2609.03826/) (2026)

## Abstract (arXiv)

In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we propose a framework---called Locally Selective Combination in Parallel Outlier Ensembles (LSCP)---which addresses the issue by defining a local region around a test instance using the consensus of its nearest neighbors in randomly selected feature subspaces. The top-performing base detectors in this local region are selected and combined as the model's final output. Four variants of the LSCP framework are compared with seven widely used parallel frameworks. Experimental results demonstrate that one of these variants, LSCP_AOM, consistently outperforms baselines on the majority of twenty real-world datasets.