# A Generative Model for Self/Non-self Discrimination in Strings

@inproceedings{Pll2009AGM, title={A Generative Model for Self/Non-self Discrimination in Strings}, author={Matti P{\"o}ll{\"a}}, booktitle={ICANNGA}, year={2009} }

A statistical model is presented as an alternative to negative selection in anomaly detection of discrete data. We extend the use of probabilistic generative models from fixed-length binary strings into variable-length strings from a finite symbol alphabet using a mixture model of multinomial distributions for the frequency of adjacent symbols in a sliding window over a string. Robust and localized change analysis of text corpora is viewed as an application area.

## 3 Citations

### On using an ensemble approach of AIS and SVM for text classification

- Computer Science
- 2010

An hybrid system for text classification based on the ensemble of both AIS and SVM approaches is presented, resulting in a classifica tion that improves upon all baseline contributors of the ensembl e committee.

### A Hybrid AIS-SVM Ensemble Approach for Text Classification

- Computer ScienceICANNGA
- 2011

An ensemble-based structure that includes Support Vector Machines and Artificial Immune Systems is put forward, using a heterogeneous ensemble to improve overall performance, including a confidence on each system classification as the differentiating factor.

### Negative Selection of Written Language Using Character Multiset Statistics

- Computer ScienceJournal of Computer Science and Technology
- 2010

Theoretical analysis on ergodic Markov chains is used to outline the properties of the presented anomaly detection algorithm and the probability of successful detection andSimulations are used to evaluate the detection sensitivity and the resolution of the analysis on both generated artificial data and real-world language data including the English Wikipedia.

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