| Type: | Package |
| Title: | Class Cover Catch Digraph Classification |
| Version: | 0.3.2 |
| Description: | Fit Class Cover Catch Digraph Classification models that can be used in machine learning. Pure and proper and random walk approaches are available. Methods are explained in Priebe et al. (2001) <doi:10.1016/S0167-7152(01)00129-8>, Priebe et al. (2003) <doi:10.1007/s00357-003-0003-7>, and Manukyan and Ceyhan (2016) <doi:10.48550/arXiv.1904.04564>. |
| Depends: | R (≥ 4.2) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| LinkingTo: | Rcpp, RcppArmadillo |
| Imports: | Rcpp, RANN, Rfast, proxy |
| NeedsCompilation: | yes |
| Packaged: | 2023-04-22 15:24:40 UTC; Fatih |
| Author: | Fatih Saglam [aut, cre] |
| Maintainer: | Fatih Saglam <saglamf89@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-04-24 09:50:02 UTC |
Pure and Proper Class Cover Catch Digraph Classifier
Description
pcccd_classifier fits a Pure and Proper Class Cover CatchDigraph (PCCCD) classification model.
Usage
pcccd_classifier(x, y, proportion = 1)Arguments
x | feature matrix or dataframe. |
y | class factor variable. |
proportion | proportion of covered samples. A real number between |
Details
Multiclass framework for PCCCD. PCCCD determines target class dominant pointssetS and their circular cover area by determining ballsB(x^{\text{target}}, r_{i}) with radii r using minimum amount ofdominant point which satisfiesX^{\text{non-target}}\cap \bigcup_{i}B_{i} = \varnothing (pure) andX^{\text{target}}\subset \bigcup_{i}B_{i} (proper).
This guarantees that balls of target class never covers any non-targetsamples (pure) and balls cover all target samples (proper).
For detail, please refer to Priebe et al. (2001), Priebe et al. (2003),and Manukyan and Ceyhan (2016).
Note: Much faster thancccd package.
Value
an object of "cccd_classifier" which includes:
i_dominant_list | dominant sample indexes. |
x_dominant_list | dominant samples from feature matrix, x |
radii_dominant_list | Radiuses of the circle for dominant samples |
class_names | class names |
k_class | number of classes |
proportions | proportions each class covered |
Author(s)
Fatih Saglam, saglamf89@gmail.com
References
Priebe, C. E., DeVinney, J., & Marchette, D. J. (2001). On the distributionof the domination number for random class cover catch digraphs. Statistics &Probability Letters, 55(3), 239–246. https://doi.org/10.1016/s0167-7152(01)00129-8
Priebe, C. E., Marchette, D. J., DeVinney, J., & Socolinsky, D. A. (2003).Classification Using Class Cover Catch Digraphs. Journal of Classification,20(1), 3–23. https://doi.org/10.1007/s00357-003-0003-7
Manukyan, A., & Ceyhan, E. (2016). Classification of imbalanced data with ageometric digraph family. Journal of Machine Learning Research, 17(1),6504–6543. https://jmlr.org/papers/volume17/15-604/15-604.pdf
Examples
n <- 1000x1 <- runif(n, 1, 10)x2 <- runif(n, 1, 10)x <- cbind(x1, x2)y <- as.factor(ifelse(3 < x1 & x1 < 7 & 3 < x2 & x2 < 7, "A", "B"))m_pcccd <- pcccd_classifier(x = x, y = y)# datasetplot(x, col = y, asp = 1)# dominant samples of first classx_center <- m_pcccd$x_dominant_list[[1]]# radii of balls for first classradii <- m_pcccd$radii_dominant_list[[1]]# ballsfor (i in 1:nrow(x_center)) {xx <- x_center[i, 1]yy <- x_center[i, 2]r <- radii[i]theta <- seq(0, 2*pi, length.out = 100)xx <- xx + r*cos(theta)yy <- yy + r*sin(theta)lines(xx, yy, type = "l", col = "green")}# testing the performancei_train <- sample(1:n, round(n*0.8))x_train <- x[i_train,]y_train <- y[i_train]x_test <- x[-i_train,]y_test <- y[-i_train]m_pcccd <- pcccd_classifier(x = x_train, y = y_train)pred <- predict(object = m_pcccd, newdata = x_test)# confusion matrixtable(y_test, pred)# test accuracysum(y_test == pred)/nrow(x_test)Pure and Proper Class Cover Catch Digraph Prediction
Description
predict.pcccd_classifier makes prediction usingpcccd_classifier object.
Usage
## S3 method for class 'pcccd_classifier'predict(object, newdata, type = "pred", ...)Arguments
object | a |
newdata | newdata as matrix or dataframe. |
type | "pred" or "prob". Default is "pred". "pred" is class estimations,"prob" is |
... | not used. |
Details
Estimations are based on nearest dominant neighbor in radius unit.
For detail, please refer to Priebe et al. (2001), Priebe et al. (2003),and Manukyan and Ceyhan (2016).
Value
a vector of class predictions (if type is "pred") or an\times pmatrix of class probabilities (if type is "prob").
Author(s)
Fatih Saglam, saglamf89@gmail.com
References
Priebe, C. E., DeVinney, J., & Marchette, D. J. (2001). On the distributionof the domination number for random class cover catch digraphs. Statistics &Probability Letters, 55(3), 239–246. https://doi.org/10.1016/s0167-7152(01)00129-8
Priebe, C. E., Marchette, D. J., DeVinney, J., & Socolinsky, D. A. (2003).Classification Using Class Cover Catch Digraphs. Journal of Classification,20(1), 3–23. https://doi.org/10.1007/s00357-003-0003-7
Manukyan, A., & Ceyhan, E. (2016). Classification of imbalanced data with ageometric digraph family. Journal of Machine Learning Research, 17(1),6504–6543. https://jmlr.org/papers/volume17/15-604/15-604.pdf
Examples
n <- 1000x1 <- runif(n, 1, 10)x2 <- runif(n, 1, 10)x <- cbind(x1, x2)y <- as.factor(ifelse(3 < x1 & x1 < 7 & 3 < x2 & x2 < 7, "A", "B"))# testing the performancei_train <- sample(1:n, round(n*0.8))x_train <- x[i_train,]y_train <- y[i_train]x_test <- x[-i_train,]y_test <- y[-i_train]m_pcccd <- pcccd_classifier(x = x_train, y = y_train)pred <- predict(object = m_pcccd, newdata = x_test)# confusion matrixtable(y_test, pred)# test accuracysum(y_test == pred)/nrow(x_test)Random Walk Class Cover Catch Digraph Prediction
Description
predict.rwcccd_classifier makes prediction usingrwcccd_classifier object.
Usage
## S3 method for class 'rwcccd_classifier'predict(object, newdata, type = "pred", e = 0, ...)Arguments
object | a |
newdata | newdata as matrix or dataframe. |
type | "pred" or "prob". Default is "pred". "pred" is class estimations,"prob" is |
e | 0 or 1. Default is 0. Penalty based on |
... | not used. |
Details
Estimations are based on nearest dominant neighbor in radius unit.e argument is used to penalize estimations based onT scores inrwcccd_classifier object.
For detail, please refer to Priebe et al. (2001), Priebe et al. (2003),and Manukyan and Ceyhan (2016).
Value
a vector of class predictions (if type is "pred") or an\times pmatrix of class probabilities (if type is "prob").
Author(s)
Fatih Saglam, saglamf89@gmail.com
References
Priebe, C. E., DeVinney, J., & Marchette, D. J. (2001). On the distributionof the domination number for random class cover catch digraphs. Statistics &Probability Letters, 55(3), 239–246. https://doi.org/10.1016/s0167-7152(01)00129-8
Priebe, C. E., Marchette, D. J., DeVinney, J., & Socolinsky, D. A. (2003).Classification Using Class Cover Catch Digraphs. Journal of Classification,20(1), 3–23. https://doi.org/10.1007/s00357-003-0003-7
Manukyan, A., & Ceyhan, E. (2016). Classification of imbalanced data with ageometric digraph family. Journal of Machine Learning Research, 17(1),6504–6543. https://jmlr.org/papers/volume17/15-604/15-604.pdf
Examples
n <- 1000x1 <- runif(n, 1, 10)x2 <- runif(n, 1, 10)x <- cbind(x1, x2)y <- as.factor(ifelse(3 < x1 & x1 < 7 & 3 < x2 & x2 < 7, "A", "B"))# testing the performancei_train <- sample(1:n, round(n*0.8))x_train <- x[i_train,]y_train <- y[i_train]x_test <- x[-i_train,]y_test <- y[-i_train]m_rwcccd <- rwcccd_classifier(x = x_train, y = y_train)pred <- predict(object = m_rwcccd, newdata = x_test, e = 0)# confusion matrixtable(y_test, pred)# test accuracysum(y_test == pred)/nrow(x_test)Random Walk Class Cover Catch Digraph Classifier
Description
rwcccd_classifier andrwcccd_classifier_2 fits aRandom Walk Class Cover Catch Digraph (RWCCCD) classification model.rwcccd_classifier uses C++ for speed andrwcccd_classifier_2uses R language to determine balls.
Usage
rwcccd_classifier(x, y, method = "default", m = 1, proportion = 0.99)rwcccd_classifier_2( x, y, method = "default", m = 1, proportion = 0.99, partial_ordering = FALSE)Arguments
x | feature matrix or dataframe. |
y | class factor variable. |
method | "default" or "balanced". |
m | penalization parameter. Takes value in |
proportion | proportion of covered samples. A real number between |
partial_ordering |
|
Details
Random Walk Class Cover Catch Digraphs (RWCCD) are determined by calculatingT_{\text{target}} score for each class as target class as
T_{\text{target}}=R_{\text{target}}(r_{\text{target}})-\frac{r_{\text{target}}n_u}{2d_m(x)}.
Here,r_{\text{target}} is radius and determined by maximumR_{\text{target}}(r) - P_{\text{target}}(r) calculated for each target sample.R_{\text{target}}(r) is
R_{\text{target}}(r):= w_{target}|{z\in X^{\text{target}}_{n_{\text{target}}}:d(x^{\text{target}},z)\leq r}| - w_{non-target}|{z\in X^{\text{non-target}}_{n_{\text{non-target}}}:d(x^{\text{target}},z)\leq r}|
andP_{\text{target}}(r) is
P_{\text{target}}(r) = m\times d(x^{\text{target}},z)^p.
m=0 removes penalty.w_{target}=1 for default andw_{target}=n_{\text{target}/n_{\text{non-target}}} for balanced method.n_u is the number of uncovered samples in the current iteration andd_m(x) is\max{d(x^{\text{target}},x^{\text{uncovered}})}.
This method is more robust to noise compared to PCCCD However, balls coversclasses improperly andr = 0 can be selected.
For detail, please refer to Priebe et al. (2001), Priebe et al. (2003),and Manukyan and Ceyhan (2016).
Value
a rwcccd_classifier object
i_dominant_list | dominant sample indexes. |
x_dominant_list | dominant samples from feature matrix, x |
radii_dominant_list | Radiuses of the circle for dominant samples |
class_names | class names |
k_class | number of classes |
proportions | proportions each class covered |
Author(s)
Fatih Saglam, saglamf89@gmail.com
References
Priebe, C. E., DeVinney, J., & Marchette, D. J. (2001). On the distributionof the domination number for random class cover catch digraphs. Statistics &Probability Letters, 55(3), 239–246. https://doi.org/10.1016/s0167-7152(01)00129-8
Priebe, C. E., Marchette, D. J., DeVinney, J., & Socolinsky, D. A. (2003).Classification Using Class Cover Catch Digraphs. Journal of Classification,20(1), 3–23. https://doi.org/10.1007/s00357-003-0003-7
Manukyan, A., & Ceyhan, E. (2016). Classification of imbalanced data with ageometric digraph family. Journal of Machine Learning Research, 17(1),6504–6543. https://jmlr.org/papers/volume17/15-604/15-604.pdf
Examples
n <- 500x1 <- runif(n, 1, 10)x2 <- runif(n, 1, 10)x <- cbind(x1, x2)y <- as.factor(ifelse(3 < x1 & x1 < 7 & 3 < x2 & x2 < 7, "A", "B"))# datasetm_rwcccd_1 <- rwcccd_classifier(x = x, y = y, method = "default", m = 1)plot(x, col = y, asp = 1, main = "default")# dominant samples of second classx_center <- m_rwcccd_1$x_dominant_list[[2]]# radii of balls for second classradii <- m_rwcccd_1$radii_dominant_list[[2]]# ballsfor (i in 1:nrow(x_center)) { xx <- x_center[i, 1] yy <- x_center[i, 2] r <- radii[i] theta <- seq(0, 2*pi, length.out = 100) xx <- xx + r*cos(theta) yy <- yy + r*sin(theta) lines(xx, yy, type = "l", col = "green")}# datasetm_rwcccd_2 <- rwcccd_classifier_2(x = x, y = y, method = "default", m = 1, partial_ordering = TRUE)plot(x, col = y, asp = 1, main = "default, prartial_ordering = TRUE")# dominant samples of second classx_center <- m_rwcccd_2$x_dominant_list[[2]]# radii of balls for second classradii <- m_rwcccd_2$radii_dominant_list[[2]]# ballsfor (i in 1:nrow(x_center)) { xx <- x_center[i, 1] yy <- x_center[i, 2] r <- radii[i] theta <- seq(0, 2*pi, length.out = 100) xx <- xx + r*cos(theta) yy <- yy + r*sin(theta) lines(xx, yy, type = "l", col = "green")}# datasetm_rwcccd_3 <- rwcccd_classifier(x = x, y = y, method = "balanced", m = 1, proportion = 0.5)plot(x, col = y, asp = 1, main = "balanced, proportion = 0.5")# dominant samples of second classx_center <- m_rwcccd_3$x_dominant_list[[2]]# radii of balls for second classradii <- m_rwcccd_3$radii_dominant_list[[2]]# ballsfor (i in 1:nrow(x_center)) { xx <- x_center[i, 1] yy <- x_center[i, 2] r <- radii[i] theta <- seq(0, 2*pi, length.out = 100) xx <- xx + r*cos(theta) yy <- yy + r*sin(theta) lines(xx, yy, type = "l", col = "green")}# testing the performancei_train <- sample(1:n, round(n*0.8))x_train <- x[i_train,]y_train <- y[i_train]x_test <- x[-i_train,]y_test <- y[-i_train]m_rwcccd <- rwcccd_classifier(x = x_train, y = y_train, method = "balanced")pred <- predict(object = m_rwcccd, newdata = x_test)# confusion matrixtable(y_test, pred)# accuracysum(y_test == pred)/nrow(x_test)