Accuracy Evaluation of C4.5 and Naive Bayes Classifiers Using Attribute Ranking Method

Sivakumari, S.; Priyadarsini, R. Praveena; Amudha, P.

doi:10.2991/jnmp.2009.2.1.7

Cited by 3 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table III the average generalization performance (with standard deviation) over the 11 datasets with 5 models for every boosting algorithm are shown. For the purpose of more extensive comparison, we introduce other evaluation criterions (including recall, fscore, fp_rate, specificity, matthews 24,29,30 ).It's difficult to list all the results of 5 algorithms, so we just show the results of AdaBoost, LPBoost, StrongLPBoost Table 4). Note that except for the heart and diabetes datasets, the performance of StrongLPBoost is better than other boosting algorithms in almost all cases.…”

Section: Accuracymentioning

confidence: 98%

LPBoost with Strong Classifiers

Fang¹,

Fu²,

Sun³

et al. 2010

International Journal of Computational Intelligence Systems

View full text Add to dashboard Cite

The goal of boosting algorithm is to maximize the minimum margin on sample set. Based on minimax theory, the goal can be converted into minimize the maximum edge. This idea motivates LPBoost and its variants (including TotalBoost, SoftBoost, ERLPBoost) which solve the optimization problem by linear programming. These algorithms ignore the strong classifier and just minimize the maximum edge of weak classifiers so that all the edges of weak classifier are at most γ .This paper shows that the edge of strong classifier may be higher than the maximum edge of weak classifiers and proposes a novel boosting algorithm which introduced strong classifier into the optimization problem and constrained the edges of both weak and strong classifiers no more than γ . Furthermore, we justified the reasonability of introducing strong classifier using minimax theory.We compared our algorithm with other approaches including AdaBoost, LPBoost, TotalBoost, SoftBoost, and ERLPBoost on the UCI benchmark dataset. In simulation studies we show that our algorithm converges faster than SoftBoost and ERLPBoost. In a benchmark comparison we illustrate the competiveness of our approach from the aspect of time consuming, and generalization error.

show abstract

Section: Accuracymentioning

confidence: 98%

LPBoost with Strong Classifiers

Fang¹,

Fu²,

Sun³

et al. 2010

International Journal of Computational Intelligence Systems

View full text Add to dashboard Cite

show abstract

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Nahid

Kong

2017

Computational and Mathematical Methods in Medicine

106

View full text Add to dashboard Cite

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

show abstract

Research on discovering deep web entries

Wang

Zuo

et al. 2011

COMSIS J

View full text Add to dashboard Cite

Ontology plays an important role in locating Domain-Specific Deep Web contents, therefore, this paper presents a novel framework WFF for efficiently locating Domain-Specific Deep Web databases based on focused crawling and ontology by constructing Web Page Classifier(WPC), Form Structure Classifier(FSC) and Form Content Classifier(FCC) in a hierarchical fashion. Firstly, WPC discovers potentially interesting pages based on ontology-assisted focused crawler. Then, FSC analyzes the interesting pages and determines whether these pages subsume searchable forms based on structural characteristics. Lastly, FCC identifies searchable forms that belong to a given domain in the semantic level, and stores these URLs of Domain- Specific searchable forms to a database. Through a detailed experimental evaluation, WFF framework not only simplifies discovering process, but also effectively determines Domain-Specific databases.

show abstract

Accuracy Evaluation of C4.5 and Naive Bayes Classifiers Using Attribute Ranking Method

Cited by 3 publications

References 7 publications

LPBoost with Strong Classifiers

LPBoost with Strong Classifiers

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Research on discovering deep web entries

Contact Info

Product

Resources

About