| Title: | A k Nearest Neibour Ensemble Based on Extended Neighbourhood Rule |
|---|---|
| Description: | The extended neighbourhood rule for the k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Saeed Aldahmani, Zardad Khan (2022) <doi:10.48550/arXiv.2205.15111>. |
| Authors: | Amjad Ali [aut, cre, cph], Muhammad Hamraz [aut], Saeed Aldahmani [aut], Zardad Khan [aut] |
| Maintainer: | Amjad Ali <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.1 |
| Built: | 2024-12-07 06:32:27 UTC |
| Source: | CRAN |
The function ExNRule() gives predictions and class probabilities for binary classification data using the extended neighbourhood rule (ExNRule) for k nearest neibour (kNN) ensemble.
ExNRule(xtrain, xtest, ytrain, k=3, r=500, p=round(sqrt(ncol(xtrain))))ExNRule(xtrain, xtest, ytrain, k=3, r=500, p=round(sqrt(ncol(xtrain))))
xtrain |
The features space of the training dataset. |
xtest |
The feature space of the testing data. |
ytrain |
The response variable of the training dataset. |
k |
The nearest observation in the extended pattern. |
r |
The total number of base kNN models using ExNRule. |
p |
The number of features used for each base kNN model using ExNRule. |
class |
Predicted classes for the test data. |
class.prob |
Predicted class probabilities for the test data. |
Amjad Ali, Muhammad Hamraz, Saeed Aldahmani, Zardad Khan.
Maintainer: Amjad Ali <[email protected]>
Ali, Amjad, et al. "An Optimal k Nearest Neighbours Ensemble for Classification Based on Extended Neighbourhood Rule with Features subspace." arXiv preprint arXiv:2211.11278 (2022).
data(ILPD) X <- ILPD[,-11] Y <- as.factor(ILPD[,11]) n <- nrow(ILPD) train <- sample(1:n, 0.7*n, replace = FALSE) xtrain <- X[train,] xtest <- X[-train,] ytrain <- Y[train] ytest <- Y[-train] mymod <- ExNRule(xtrain, xtest, ytrain, k=3, r=500, p=round(sqrt(ncol(xtrain)))) pred.class <- mymod$class class.prob <- mymod$class.prob Accuracy <- mean(pred.class==ytest) ytestnum <- as.numeric(as.factor(ytest))-1 BrierScore <- mean((ytestnum-class.prob)^2) cbind(Accuracy, BrierScore)data(ILPD) X <- ILPD[,-11] Y <- as.factor(ILPD[,11]) n <- nrow(ILPD) train <- sample(1:n, 0.7*n, replace = FALSE) xtrain <- X[train,] xtest <- X[-train,] ytrain <- Y[train] ytest <- Y[-train] mymod <- ExNRule(xtrain, xtest, ytrain, k=3, r=500, p=round(sqrt(ncol(xtrain)))) pred.class <- mymod$class class.prob <- mymod$class.prob Accuracy <- mean(pred.class==ytest) ytestnum <- as.numeric(as.factor(ytest))-1 BrierScore <- mean((ytestnum-class.prob)^2) cbind(Accuracy, BrierScore)
This data set contains 416 liver patient records and 167 non liver patient records.The data set was collected from north east of Andhra Pradesh, India. The class label divides the patients into 2 groups i.e. liver patient or not (1, 0). This data set contains 441 male patient records and 142 female patient records.
data("ILPD")data("ILPD")
A data frame with 582 observations on the following 11 variables.
v1Age of the patient. Any patient whose age exceeded 89 is listed as being of age "90".
v2Gender of the patients (male = 1, female = 0).
v3Total Bilirubin.
v4Direct Bilirubin.
v5Alkphos Alkaline Phosphatase.
v6Sgpt Alanine Aminotransferase.
v7Sgot Aspartate Aminotransferase.
v8Total Proteins.
v9Albumin.
v10A/G Ratio Albumin and Globulin Ratio.
ClassLiver patient or not (1, 0).
https://www.openml.org/search?type=data&sort=runs&id=1480&status=active
1. Bendi Venkata Ramana, Prof. M. S. Prasad Babu and Prof. N. B. Venkateswarlu, A Critical Comparative Study of Liver Patients from USA and INDIA: An Exploratory Analysis., International Journal of Computer Science Issues, ISSN:1694-0784, May 2012.
2. Bendi Venkata Ramana, Prof. M. S. Prasad Babu and Prof. N. B. Venkateswarlu, A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis, International Journal of Database Management Systems (IJDMS), Vol.3, No.2, ISSN : 0975-5705, PP 101-114, May 2011.
data(ILPD) str(ILPD)data(ILPD) str(ILPD)