Programming Fault forecast has turned out to be most essential in programming Development uncommonly in programming Testing. The exact extrapolation of issues in conundrum can help to patch test effort, which decreases expenses and repair the nature of programming. Issue forecast model utilizing object situated measurements for code, datasets as info qualities to anticipate the issue probability by Naïve Bayes Classifier and these mock-ups have been far and wide utilized for bunching and grouping likewise exceptionally flawless eccentric to Bayesian systems for expansive range likelihood evaluation, generally in shortcoming expectation. In this paper, Naive Bayes classifier has been actualized on different consistent datasets.
목차
Abstract 1. Introduction 1.2. Classification 1.3. Rule-Based Classification 1.4. Decision Tree Classification 1.5. Bayesian Classification 2. Related Works 3. Motivation and Uniqueness of Work 4. Proposed Scheme 5. Methodology 5.1. Datasets 5.2. Confusion Matrix: 6. Result 7. Conclusion and Imminent Work References
키워드
NASA (MDP) repositoryDatasetKc1Kc2Naïve Bayes
저자
M. Vijaya Bharathi [ CSE Department, GMRIT, Rajam & Research Scholar @GITAM University ]
Rodda Sireesha [ Department of CSE GITAM Visakhapatnam, AP, India ]
Corresponding author
보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Software Engineering and Its Applications
간기
월간
pISSN
1738-9984
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.10 No.8