Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets

Mushtaq, Zohaib; Yaqub, Akbari; Sani, Shaima; Khalid, Adnan

doi:10.1080/02533839.2019.1676658

Cited by 51 publications

(32 citation statements)

References 26 publications

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“…Anyhow we already found a high enough accuracy, as close to 100% (98.8327%) as possible, with the EbkNN method. This accuracy is the best from amo n g st t h e wo rks o f other researchers on WDBC dataset [35,36,37,38,39,40,41,42,43] that the authors of this paper kno w o f. Th e wo rk b y [43] is the closest that it gets to our work. Their best accuracy is 98.62% for kNN with chi-square based feature selection.…”

Section: Discussionmentioning

confidence: 54%

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Sinha

Sinha²

2020

AJAS

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Entropy based k-Nearest Neighbor pattern classification (EbkNN) is a variation of the conventional k-Nearest Neighbor rule of pattern classification, which exclusively optimizes the value of k-neighbors for each test data based on the calculations of entropy. The formula for entropy used in EbkNN is the one that has been defined popularly in information theory for a set of n different types of information (class) attached to a total of m objects (data points) with each object defined by f features. In EbkNN that value of k is chosen for discrimination of given test data for which the entropy is the least non-zero value. Other rules of conventional kNN are retained in EbkNN. It is concluded that EbkNN works best for binary classification. It is computationally prohibitive to use EbkNN for discriminating the data points of the test dataset into number of classes greater than two. The biggest advantage of EbkNN vis-à-vis the conventional kNN is that in one single run of EbkNN algorithm we get optimum classification of test data. But conventional kNN algorithm has to be run separately for each of the selected range of values of k, and then the optimum k to be chosen from amongst them. We also tested our EbkNN method on WDBC (Wisconsin Diagnostic Breast Cancer) dataset. There are 569 instances in this dataset and we made a random choice of first 290 instances as training dataset and the rest 279 instances as test dataset. We got an exceptionally remarkable result with EbkNN method- accuracy close to 100% and better than the ones got by most of the other researchers who worked on WDBC dataset.

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Section: Discussionmentioning

confidence: 54%

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Sinha

Sinha²

2020

AJAS

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“…This requires the use of ML algorithms for accurate prediction. Mushtaq et al [14] used the KNN algorithm for breast cancer classification. It provides a method for determining the distance between two sets of data.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

et al. 2021

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Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain.

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“…ANN, SVM and Decision tree are compared in [7] and SVM was the best among all the machine learning methods with highest accuracy and lowest error rate. In [8] authors explored KNN performance with WBC(wisconsin Breast Cancer) dataset and WDBC(wisconsin diagnostic breast cancer) dataset with three iterartions, in which the initial iteration is without feature selection, second with feature selection and KNN and the last iteration consist of Chi-sqaure feature selection, all these help in getting optimal value of K and also the chi-square base feature selection with KNN classifier gives the best accuracy results.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

Gupta*¹,

Gill²

2020

IJITEE

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Breast Cancer is one of the most deadly disease and most of the women are infected by this vital disease in many parts of the world. Medical tests conducted in hospitals for determining the disease are very much expensive as well as timeconsuming. The problem can be resolved by diagnosing the problem in early spam of time and by providing results with more accuracy.In this paper, different machine learning and neural network algorithmhave been studied and compared to predict cancer in early stages so that life can be saved. The dataset available publically for Breast Cancer has been used. Different algorithms compared includeSupport Vector Machine Classification (SVM), K-Nearest NeighborClassification (KNN), Decision tree Classification (DT), Random Forest Classification (RF) and Extreme Learning Machine (ELM).All are compared on the basis of Accuracy and processing time are considered as the parameters for comparing analysis. The results reveal that extreme learning machine comes to be the better algorithm.

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Effective K-nearest neighbor classifications for Wisconsin breast cancer data sets

Cited by 51 publications

References 26 publications

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction

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