A practical study on shape space and its occupancy in negative selection

Research output: A Conference proceeding or a Chapter in BookConference contribution

3 Citations (Scopus)

Abstract

The success of a negative selection algorithm depends on its detectors. A shape space, conceptually, is where selves, detectors, and antigens reside. These detectors are expected to fully cover the whole shape space. The better the coverage; the better the detection rate. However, this assumption brings a major challenge to negative selection experiments - the scalability problem, where the experiments cannot process real life datasets in a timely manner. On the other hand, with any real life dataset, due to arbitrary antibody/antigen representing formats, the shape space actually cannot be fully occupied. The unoccupied locations sometimes constitute a significant, or even overwhelm, portion in a shape space. In this paper, we first briefly review the theoretic model of the shape space and then study the impact of the unoccupied locations, under the term shape space occupancy. Based on the study outcomes, we suggest the heuristics for generating detectors. We demonstrate shape space occupancy, detector generation by antigen feedback mechanism, and negative selection experiments on 4 different datasets, which cover the data presentation formats in both strings and real number valued vectors.
Original languageEnglish
Title of host publicationWCCI 2010: IEEE World Congress on Computational Intelligence
Place of PublicationPiscataway, N.J., USA
PublisherIEEE
Pages623-629
Number of pages7
ISBN (Print)9781424481262
DOIs
Publication statusPublished - 2010
Event2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010) - Barcelona, Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Conference

Conference2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010)
CountrySpain
CityBarcelona
Period18/07/1023/07/10

Fingerprint

Detectors
Antigens
Experiments
Antibodies
Scalability
Feedback

Cite this

Ma, W., Tran, D., & Sharma, D. (2010). A practical study on shape space and its occupancy in negative selection. In WCCI 2010: IEEE World Congress on Computational Intelligence (pp. 623-629). Piscataway, N.J., USA: IEEE. https://doi.org/10.1109/CEC.2010.5586266
Ma, Wanli ; Tran, Dat ; Sharma, Dharmendra. / A practical study on shape space and its occupancy in negative selection. WCCI 2010: IEEE World Congress on Computational Intelligence. Piscataway, N.J., USA : IEEE, 2010. pp. 623-629
@inproceedings{141bd0eb9070463e9f493eabb79d39e4,
title = "A practical study on shape space and its occupancy in negative selection",
abstract = "The success of a negative selection algorithm depends on its detectors. A shape space, conceptually, is where selves, detectors, and antigens reside. These detectors are expected to fully cover the whole shape space. The better the coverage; the better the detection rate. However, this assumption brings a major challenge to negative selection experiments - the scalability problem, where the experiments cannot process real life datasets in a timely manner. On the other hand, with any real life dataset, due to arbitrary antibody/antigen representing formats, the shape space actually cannot be fully occupied. The unoccupied locations sometimes constitute a significant, or even overwhelm, portion in a shape space. In this paper, we first briefly review the theoretic model of the shape space and then study the impact of the unoccupied locations, under the term shape space occupancy. Based on the study outcomes, we suggest the heuristics for generating detectors. We demonstrate shape space occupancy, detector generation by antigen feedback mechanism, and negative selection experiments on 4 different datasets, which cover the data presentation formats in both strings and real number valued vectors.",
author = "Wanli Ma and Dat Tran and Dharmendra Sharma",
year = "2010",
doi = "10.1109/CEC.2010.5586266",
language = "English",
isbn = "9781424481262",
pages = "623--629",
booktitle = "WCCI 2010: IEEE World Congress on Computational Intelligence",
publisher = "IEEE",

}

Ma, W, Tran, D & Sharma, D 2010, A practical study on shape space and its occupancy in negative selection. in WCCI 2010: IEEE World Congress on Computational Intelligence. IEEE, Piscataway, N.J., USA, pp. 623-629, 2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010), Barcelona, Spain, 18/07/10. https://doi.org/10.1109/CEC.2010.5586266

A practical study on shape space and its occupancy in negative selection. / Ma, Wanli; Tran, Dat; Sharma, Dharmendra.

WCCI 2010: IEEE World Congress on Computational Intelligence. Piscataway, N.J., USA : IEEE, 2010. p. 623-629.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - A practical study on shape space and its occupancy in negative selection

AU - Ma, Wanli

AU - Tran, Dat

AU - Sharma, Dharmendra

PY - 2010

Y1 - 2010

N2 - The success of a negative selection algorithm depends on its detectors. A shape space, conceptually, is where selves, detectors, and antigens reside. These detectors are expected to fully cover the whole shape space. The better the coverage; the better the detection rate. However, this assumption brings a major challenge to negative selection experiments - the scalability problem, where the experiments cannot process real life datasets in a timely manner. On the other hand, with any real life dataset, due to arbitrary antibody/antigen representing formats, the shape space actually cannot be fully occupied. The unoccupied locations sometimes constitute a significant, or even overwhelm, portion in a shape space. In this paper, we first briefly review the theoretic model of the shape space and then study the impact of the unoccupied locations, under the term shape space occupancy. Based on the study outcomes, we suggest the heuristics for generating detectors. We demonstrate shape space occupancy, detector generation by antigen feedback mechanism, and negative selection experiments on 4 different datasets, which cover the data presentation formats in both strings and real number valued vectors.

AB - The success of a negative selection algorithm depends on its detectors. A shape space, conceptually, is where selves, detectors, and antigens reside. These detectors are expected to fully cover the whole shape space. The better the coverage; the better the detection rate. However, this assumption brings a major challenge to negative selection experiments - the scalability problem, where the experiments cannot process real life datasets in a timely manner. On the other hand, with any real life dataset, due to arbitrary antibody/antigen representing formats, the shape space actually cannot be fully occupied. The unoccupied locations sometimes constitute a significant, or even overwhelm, portion in a shape space. In this paper, we first briefly review the theoretic model of the shape space and then study the impact of the unoccupied locations, under the term shape space occupancy. Based on the study outcomes, we suggest the heuristics for generating detectors. We demonstrate shape space occupancy, detector generation by antigen feedback mechanism, and negative selection experiments on 4 different datasets, which cover the data presentation formats in both strings and real number valued vectors.

U2 - 10.1109/CEC.2010.5586266

DO - 10.1109/CEC.2010.5586266

M3 - Conference contribution

SN - 9781424481262

SP - 623

EP - 629

BT - WCCI 2010: IEEE World Congress on Computational Intelligence

PB - IEEE

CY - Piscataway, N.J., USA

ER -

Ma W, Tran D, Sharma D. A practical study on shape space and its occupancy in negative selection. In WCCI 2010: IEEE World Congress on Computational Intelligence. Piscataway, N.J., USA: IEEE. 2010. p. 623-629 https://doi.org/10.1109/CEC.2010.5586266