Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining

Sinaga, Lotar Mateus; Sawaluddin,; Suwilo, Saib

doi:10.1088/1757-899x/725/1/012106

Cited by 17 publications

(9 citation statements)

References 3 publications

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“…The KNN method finds groupings of k objects in the training data that are most alike (similar) to the objects in the new data or data testing (Sinaga and Suwilo, 2020) .…”

Section: Knn Methodsmentioning

confidence: 99%

Majority Voting as Ensemble Classifier for Cervical Cancer Classification

Desiani

Kresnawati

Arhami³

et al. 2023

Sci. Technol. Indones

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Cervical cancer is one of the deadliest female cancers. Early identification of cervical cancer through pap smear cell image evaluation is one of the strategies to reduce cervical cancer cases. The classification methods that are often used are SVM, MLP, and K-NN. The weakness of the SVM method is that it is not efficient on large datasets. Meanwhile, in the MLP method, large amounts of data can increase the complexity of each layer, thereby affecting the duration of the weighting process. Moreover, the K-NN method is not efficient for data with a large number of attributes. The ensemble method is one of the techniques to overcome the limitations of a single classification method. The ensemble classification method combines the performance of several classification methods. This study proposes an ensemble method with the majority voting that can be used in cervical cancer classification based on pap smear images in the Herlev dataset. Majority Voting is used to integrate test results from the SVM, MLP, and KNN methods by looking at the majority results on the test data classification. The results of this study indicate that the accuracy results obtained in the ensemble method increased by 1.72% compared to the average accuracy value in SVM, MLP, and KNN. for sensitivity results, the results of the ensemble method were able to increase the sensitivity increase by 0.74% compared to the average of the three single classification methods. for specificity, the ensemble method can increase the specificity results by 3.4%. From the results of the study, it can be concluded that the ensemble method with the most votes is able to improve the classification performance of the single classification method in classifying cervical cancer abnormalities with pap smear images.

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“…The KNN method finds groupings of k objects in the training data that are most alike (similar) to the objects in the new data or data testing (Sinaga and Suwilo, 2020) .…”

Section: Knn Methodsmentioning

confidence: 99%

Majority Voting as Ensemble Classifier for Cervical Cancer Classification

Desiani

Kresnawati

Arhami³

et al. 2023

Sci. Technol. Indones

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“…NB This algorithm is a generalization of the Bayesian theorem in which the attributes are assumed to be independent of one another. NB is a probabilistic model, and the process it follows involves the calculation of the probability of a data sample belonging to a particular class [56,57]. NB is sometimes called simple Bayes or independence Bayes.…”

Section: Basic ML Algorithmsmentioning

confidence: 99%

Prediction of successful aging using ensemble machine learning algorithms

Varzaneh

Yazdani

Kazemi-Arpanahi³

2022

BMC Med Inform Decis Mak

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Background Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive and qualitative view of aging. From a biomedical perspective, SA is defined as the absence of diseases or disability disorders. This is distinct from normal aging, which is associated with age-related deterioration in physical and cognitive functions. From a social perspective, SA highlights life satisfaction and individual well-being, usually attained through socialization. It is an abstract and multidimensional concept surrounded by imprecision about its definition and measurement. Our study attempted to find the most effective features of SA as defined by Rowe and Kahn's theory. The determined features were used as input parameters of six machine learning (ML) algorithms to create and validate predictive models for SA. Methods In this retrospective study, the raw data set was first pre-processed; then, based on the data of a sample of 983, five basic ML techniques including artificial neural network, decision tree, support vector machine, Naïve Bayes, and k-nearest neighbors (K-NN) with one ensemble method (that gathers 30 K-NN algorithms as weak learners) were trained. Finally, the prediction result was yielded using the majority vote method based on the output of the generated base models. Results The experimental results revealed that the predictive system has been more successful in predicting SA with a 93% precision, 92.40% specificity, 87.80% sensitivity, 90.31% F-measure, 89.62% accuracy, and a ROC of 96.10%, using a five-fold cross-validation procedure. Conclusions Our results showed that ML techniques potentially have satisfactory performance in supporting the SA-related decisions of social and health policymakers. The KNN-based ensemble algorithm is superior to the other ML models in classifying people into SA and non-SA classes.

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“…Subsequently, the classifier chain method proposed by scholars was established on the basis of the BR method combined with the relevance of tags, and the classification effect of this model was improved to some extent [22,23]. Classifier chain (CC), as a problem transformation strategy, is solved by designing a chain composed of multiple binary classifiers.…”

Section: Chain Classifiermentioning

confidence: 99%

Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

Zhang

Rao

2022

Mathematical Problems in Engineering

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In the field of machine learning, multilabel learning gradually evolved from the traditional text classification problem. In this study, a multiview multilabel via optimal classifier chain (MVMLOCC) algorithm based on the nearest-neighbor model is proposed. The algorithm model establishes a multilabel chain classifier for each view of the dataset and predicts unknown data samples by dynamically adjusting the weights of chain learners. When an unknown example is input, multiple chain classifier models are multiplied by corresponding weights to obtain the final tag set situation. The model makes full use of the relevance between multiple tags and the complementarity and integrity between multiple perspectives and can achieve better learning results. The final experimental results show that our proposed model can be applied to different multilabel classification tasks and has achieved excellent performance under different evaluation indicators. This study mainly studies the multiview and multilabel classification method and uses active learning technology to solve the problem of high label components in data collection.

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Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining

Cited by 17 publications

References 3 publications

Majority Voting as Ensemble Classifier for Cervical Cancer Classification

Majority Voting as Ensemble Classifier for Cervical Cancer Classification

Prediction of successful aging using ensemble machine learning algorithms

Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

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