Preprocessor Agent Approach to Knowledge Discovery Using Zero-R Algorithm

Inamdar, Sujit A.; Narangale, S M; Shinde, G. N.

doi:10.14569/ijacsa.2011.021212

Cited by 2 publications

(2 citation statements)

References 8 publications

(13 reference statements)

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“…ZeroR is based on a rule that works with a straightforward classifcation method on the target and prediction of the majority class. ZeroR ignores all predictors and relies on its decisions based on the majority of occurrences of an instance [36]. Te predictability power of ZeroR is negligible, but it has a signifcant role as a standard baseline for other classifers.…”

Section: Zerormentioning

confidence: 99%

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets

Nayab,

Khan,

Qamar

2023

Applied Computational Intelligence and Soft Computing

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This paper investigates the performance enhancement of base classifiers within the AdaBoost framework applied to medical datasets. Adaptive boosting (AdaBoost), being an instance of boosting, combines other classifiers to enhance their performance. We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. The experiments are carried out on five datasets from the medical domain based on various types of cancers, i.e., global cancer map (GCM), lymphoma-I, lymphoma-II, leukaemia, and embryonal tumours. The evaluation focuses on the accuracy, precision, and efficiency of the base classifiers in the AdaBoost framework. The results show that the performance of Naïve Bayes, Bayes network, and voted perceptron is highly improved compared to the rest of the base classifiers, attaining accuracies as high as 94.74%, 97.78%, and 97.78%, respectively. The results also show that in most cases, the base classifiers perform better with AdaBoost compared to their performance, i.e., for voted perceptron, the accuracy is improved up to 13.34%.For bagging, it is improved by up to 7%. This research aims to identify such base classifiers with optimal boosting capabilities within the AdaBoost framework for medical datasets. The significance of these results is that they provide insight into the performance of the base classifiers when used in the boosting framework to enhance the classification performance of classifiers in scenarios where individual classifiers do not perform up to the mark.

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Section: Zerormentioning

confidence: 99%

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets

Nayab,

Khan,

Qamar

2023

Applied Computational Intelligence and Soft Computing

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“…In this paper, eight attribute evaluators are proposed for solving feature selection problems in cervical cancer data [22]. The evaluation mode in all the evaluators is based on full training data.…”

Section: Selected Attributesmentioning

confidence: 99%

Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer

Al-Batah¹

2019

Int. J. Onl. Eng.

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<p class="0abstract">In this paper, an automatic three-phase cervical cancer diagnosis system is employed which includes feature extraction, feature selection followed by classiﬁcation. Firstly, the modified seed-based region growing (MSBRG) algorithm is implemented for automatic segmentation and feature extraction using 500 cervical cancer cells. Processes to obtain the threshold values and the initial seed location are carried out automatically using moving k-mean (MKM) algorithm and invariant moment techniques. Secondly, eight attribute evaluators are applied for selecting and ranking the features, which are Correlation-based Feature Selection, Classifier Attribute Evaluator, Correlation Attribute Evaluator, Gain Ratio, Info Gain, OneR, ReliefF, and Symmetrical Uncertainty. Finally, the classification is compared based on five classifiers: Decision Table, JRip, OneR, PART, and ZeroR. The performance of the classifiers is evaluated using 3 test options: the training percentage splits (50% to 98%), the full training data and the cross validation (2-fold to 10-fold). The experimental results prove the capability of the MSBRG algorithm as an automatic feature extraction method. Furthermore, this paper proves the ability of the ranked feature selection methods to select important features of a cervical cell, and favors the Decision Table as the best classifier for cervical cancer classification.</p>

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Preprocessor Agent Approach to Knowledge Discovery Using Zero-R Algorithm

Cited by 2 publications

References 8 publications

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets

Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer

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