# Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy

arXiv: https://arxiv.org/abs/2209.02275 · HTML: https://mlsanity.com/paper/2209.02275/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [A Non-intrusive Failure Prediction Mechanism for Deployed Optical Networks](https://mlsanity.com/paper/2101.06661/) (2021)
- [A Data Augmented Bayesian Network for Node Failure Prediction in Optical Networks](https://mlsanity.com/paper/2102.03616/) (2021)

## Abstract (arXiv)

This paper proposes a novel non-intrusive system failure prediction technique using available information from developers and minimal information from raw logs (rather than mining entire logs) but keeping the data entirely private with the data owners. A neural network based multi-class classifier is developed for failure prediction, using artificially generated anonymous data set, applying a combination of techniques, viz., genetic algorithm (steps), pattern repetition, etc., to train and test the network. The proposed mechanism completely decouples the data set used for training process from the actual data which is kept private. Moreover, multi-criteria decision making (MCDM) schemes are used to prioritize failures meeting business requirements. Results show high accuracy in failure prediction under different parameter configurations. On a broader context, any classification problem, beyond failure prediction, can be performed using the proposed mechanism with artificially generated data set without looking into the actual data as long as the input features can be translated to binary values (e.g. output from private binary classifiers) and can provide classification-as-a-service.