| Title: | Isotonic Boosting Classification Rules |
|---|---|
| Description: | In classification problems a monotone relation between some predictors and the classes may be assumed. In this package 'isoboost' we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. |
| Authors: | David Conde [aut, cre], Miguel A. Fernandez [aut], Cristina Rueda [aut], Bonifacio Salvador [aut] |
| Maintainer: | David Conde <[email protected]> |
| License: | GPL-2 | GPL-3 |
| Version: | 1.0.1 |
| Built: | 2024-12-04 07:10:51 UTC |
| Source: | CRAN |
In this package we present new boosting classification rules based on LogitBoost when it can be assumed that higher (or lower) values of some predictors are related to higher levels of the response.
Package: isoboost
Type: Package
Version: 1.0.1
Date: 2021-05-01
License: GPL-2 | GPL-3
For a complete list of functions with individual help pages, use library(help = "isoboost").
David Conde, Miguel A. Fernandez, Cristina Rueda, Bonifacio Salvador
Maintainer: David Conde <[email protected]>
Train and predict logitboost-based classification algorithm using multivariate isotonic regression (linear regression for no monotone features) as weak learners, based on the adjacent-categories logistic model (see Agresti (2010)). For full details on this algorithm, see Conde et al. (2020).
amilb(xlearn, ...) ## S3 method for class 'formula' amilb(formula, data, ...) ## Default S3 method: amilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)amilb(xlearn, ...) ## S3 method for class 'formula' amilb(formula, data, ...) ## Default S3 method: amilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)
formula |
A formula of the form |
data |
Data frame from which variables specified in |
xlearn |
(Required if no formula is given as the principal argument.) A data frame or matrix containing the explanatory variables. |
ylearn |
(Required if no formula is given as the principal argument.) A numeric vector or factor with numeric levels specifying the class for each observation. |
xtest |
A data frame or matrix of cases to be classified, containing the features used in |
mfinal |
Maximum number of iterations of the algorithm. |
monotone_constraints |
Numerical vector consisting of 1, 0 and -1, its length equals the number of features in |
prior |
The prior probabilities of class membership. If unspecified, equal prior probabilities are used. If present, the probabilities must be specified in the order of the factor levels. |
... |
Arguments passed to or from other methods. |
A list containing the following components:
call |
The (matched) function call. |
trainset |
Matrix with the training set used (first columns) and the class for each observation (last column). |
prior |
Prior probabilities of class membership used. |
apparent |
Apparent error rate. |
mfinal |
Number of iterations of the algorithm. |
loglikelihood |
Log-likelihood. |
posterior |
Posterior probabilities of class membership for |
class |
Labels of the class with maximal probability for |
This function may be called using either a formula and data frame, or a data frame and grouping variable, or a matrix and grouping variable as the first two arguments. All other arguments are optional.
Classes must be identified, either in a column of data or in the ylearn vector, by natural numbers varying from 1 to the number of classes. The number of classes must be greater than 1.
If there are missing values in either data, xlearn or ylearn, corresponding observations will be deleted.
David Conde
Agresti, A. (2010). Analysis of Ordinal Categorical Data, 2nd edition. John Wiley and Sons. New Jersey.
Conde, D., Fernandez, M. A., Rueda, C., and Salvador, B. (2020). Isotonic boosting classification rules. Advances in Data Analysis and Classification, 1-25.
data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- amilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 100, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 15.41219data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- amilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 100, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 15.41219
Train and predict logitboost-based classification algorithm using isotonic regression (decision stumps for no monotone features) as weak learners, based on the adjacent-categories logistic model (see Agresti (2010)). For full details on this algorithm, see Conde et al. (2020).
asilb(xlearn, ...) ## S3 method for class 'formula' asilb(formula, data, ...) ## Default S3 method: asilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)asilb(xlearn, ...) ## S3 method for class 'formula' asilb(formula, data, ...) ## Default S3 method: asilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)
formula |
A formula of the form |
data |
Data frame from which variables specified in |
xlearn |
(Required if no formula is given as the principal argument.) A data frame or matrix containing the explanatory variables. |
ylearn |
(Required if no formula is given as the principal argument.) A numeric vector or factor with numeric levels specifying the class for each observation. |
xtest |
A data frame or matrix of cases to be classified, containing the features used in |
mfinal |
Number of iterations of the algorithm. |
monotone_constraints |
Numerical vector consisting of 1, 0 and -1, its length equals the number of features in |
prior |
The prior probabilities of class membership. If unspecified, equal prior probabilities are used. If present, the probabilities must be specified in the order of the factor levels. |
... |
Arguments passed to or from other methods. |
A list containing the following components:
call |
The (matched) function call. |
trainset |
Matrix with the training set used (first columns) and the class for each observation (last column). |
prior |
Prior probabilities of class membership used. |
apparent |
Apparent error rate. |
mfinal |
Number of iterations of the algorithm. |
loglikelihood |
Log-likelihood. |
posterior |
Posterior probabilities of class membership for |
class |
Labels of the class with maximal probability for |
This function may be called using either a formula and data frame, or a data frame and grouping variable, or a matrix and grouping variable as the first two arguments. All other arguments are optional.
Classes must be identified, either in a column of data or in the ylearn vector, by natural numbers varying from 1 to the number of classes. The number of classes must be greater than 1.
If there are missing values in either data, xlearn or ylearn, corresponding observations will be deleted.
David Conde
Agresti, A. (2010). Analysis of Ordinal Categorical Data, 2nd edition. John Wiley and Sons. New Jersey.
Conde, D., Fernandez, M. A., Rueda, C., and Salvador, B. (2020). Isotonic boosting classification rules. Advances in Data Analysis and Classification, 1-25.
data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- asilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 50, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 14.69534data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- asilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 50, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 14.69534
Train and predict logitboost-based classification algorithm using multivariate isotonic regression (linear regression for no monotone features) as weak learners, based on the cumulative probabilities logistic model (see Agresti (2010)). For full details on this algorithm, see Conde et al. (2020).
cmilb(xlearn, ...) ## S3 method for class 'formula' cmilb(formula, data, ...) ## Default S3 method: cmilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)cmilb(xlearn, ...) ## S3 method for class 'formula' cmilb(formula, data, ...) ## Default S3 method: cmilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)
formula |
A formula of the form |
data |
Data frame from which variables specified in |
xlearn |
(Required if no formula is given as the principal argument.) A data frame or matrix containing the explanatory variables. |
ylearn |
(Required if no formula is given as the principal argument.) A numeric vector or factor with numeric levels specifying the class for each observation. |
xtest |
A data frame or matrix of cases to be classified, containing the features used in |
mfinal |
Maximum number of iterations of the algorithm. |
monotone_constraints |
Numerical vector consisting of 1, 0 and -1, its length equals the number of features in |
prior |
The prior probabilities of class membership. If unspecified, equal prior probabilities are used. If present, the probabilities must be specified in the order of the factor levels. |
... |
Arguments passed to or from other methods. |
A list containing the following components:
call |
The (matched) function call. |
trainset |
Matrix with the training set used (first columns) and the class for each observation (last column). |
prior |
Prior probabilities of class membership used. |
apparent |
Apparent error rate. |
mfinal |
Number of iterations of the algorithm. |
loglikelihood |
Log-likelihood. |
posterior |
Posterior probabilities of class membership for |
class |
Labels of the class with maximal probability for |
This function may be called using either a formula and data frame, or a data frame and grouping variable, or a matrix and grouping variable as the first two arguments. All other arguments are optional.
Classes must be identified, either in a column of data or in the ylearn vector, by natural numbers varying from 1 to the number of classes. The number of classes must be greater than 1.
If there are missing values in either data, xlearn or ylearn, corresponding observations will be deleted.
David Conde
Agresti, A. (2010). Analysis of Ordinal Categorical Data, 2nd edition. John Wiley and Sons. New Jersey.
Conde, D., Fernandez, M. A., Rueda, C., and Salvador, B. (2020). Isotonic boosting classification rules. Advances in Data Analysis and Classification, 1-25.
data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- cmilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 20, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 15.77061data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- cmilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 20, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 15.77061
Train and predict logitboost-based classification algorithm using isotonic regression (decision stumps for no monotone features) as weak learners, based on the cumulative probabilities logistic model (see Agresti (2010)). For full details on this algorithm, see Conde et al. (2020).
csilb(xlearn, ...) ## S3 method for class 'formula' csilb(formula, data, ...) ## Default S3 method: csilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)csilb(xlearn, ...) ## S3 method for class 'formula' csilb(formula, data, ...) ## Default S3 method: csilb(xlearn, ylearn, xtest = xlearn, mfinal = 100, monotone_constraints = rep(0, dim(xlearn)[2]), prior = NULL, ...)
formula |
A formula of the form |
data |
Data frame from which variables specified in |
xlearn |
(Required if no formula is given as the principal argument.) A data frame or matrix containing the explanatory variables. |
ylearn |
(Required if no formula is given as the principal argument.) A numeric vector or factor with numeric levels specifying the class for each observation. |
xtest |
A data frame or matrix of cases to be classified, containing the features used in |
mfinal |
Number of iterations of the algorithm. |
monotone_constraints |
Numerical vector consisting of 1, 0 and -1, its length equals the number of features in |
prior |
The prior probabilities of class membership. If unspecified, equal prior probabilities are used. If present, the probabilities must be specified in the order of the factor levels. |
... |
Arguments passed to or from other methods. |
A list containing the following components:
call |
The (matched) function call. |
trainset |
Matrix with the training set used (first columns) and the class for each observation (last column). |
prior |
Prior probabilities of class membership used. |
apparent |
Apparent error rate. |
mfinal |
Number of iterations of the algorithm. |
loglikelihood |
Log-likelihood. |
posterior |
Posterior probabilities of class membership for |
class |
Labels of the class with maximal probability for |
This function may be called using either a formula and data frame, or a data frame and grouping variable, or a matrix and grouping variable as the first two arguments. All other arguments are optional.
Classes must be identified, either in a column of data or in the ylearn vector, by natural numbers varying from 1 to the number of classes. The number of classes must be greater than 1.
If there are missing values in either data, xlearn or ylearn, corresponding observations will be deleted.
David Conde
Agresti, A. (2010). Analysis of Ordinal Categorical Data, 2nd edition. John Wiley and Sons. New Jersey.
Conde, D., Fernandez, M. A., Rueda, C., and Salvador, B. (2020). Isotonic boosting classification rules. Advances in Data Analysis and Classification, 1-25.
data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- csilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 100, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 17.92115data(motors) table(motors$condition) ## 1 2 3 4 ## 83 67 70 60 ## Let us consider the first three variables as predictors data <- motors[, 1:3] grouping = motors$condition ## ## Lower values of the amplitudes are expected to be ## related to higher levels of damage severity, so ## we can consider the following monotone constraints monotone_constraints = rep(-1, 3) set.seed(7964) values <- runif(dim(data)[1]) trainsubset <- values < 0.2 obj <- csilb(data[trainsubset, ], grouping[trainsubset], data[-trainsubset, ], 100, monotone_constraints) ## Apparent error obj$apparent ## 4.761905 ## Error rate 100*mean(obj$class != grouping[-trainsubset]) ## 17.92115
Electrical induction motors are widely used in industry. In the industrual context, the early detection of possible damage in the motor is very important since failures can result in financial losses. Motor Current Signature Analysis is the most widespread technique to diagnose a faulty motor, see Choudhary et al. (2019). This technique is based on the spectral analysis of the stator current: motor faults cause an asymmetry that reflects as additional harmonics in the current spectrum, so side bands around the main frequency are considered and amplitudes of these side bands around odd harmonics are measured.
The data were generated by Oscar Duque and Daniel Morinigo at the Electrical Engineering laboratory of the Universidad de Valladolid.
Four condition states of damage severity are considered: 1 - undamaged, 2 - incipient fault, 3 - moderate damage, 4 - severe damage.
data(motors)data(motors)
A data frame with 280 observations on 7 variables, six are numerical and one nominal defining the condition state of the motors.
| [,1] | amplitude_l.1 | Amplitude of the first lower side band around harmonic 1 |
| [,2] | amplitude_u.1 | Amplitude of the first upper side band around harmonic 1 |
| [,3] | amplitude_l.5 | Amplitude of the first lower side band around harmonic 5 |
| [,4] | amplitude_u.5 | Amplitude of the first upper side band around harmonic 5 |
| [,5] | amplitude_l.7 | Amplitude of the first lower side band around harmonic 7 |
| [,6] | amplitude_u.7 | Amplitude of the first upper side band around harmonic 7 |
| [,7] | condition | Condition state |
Creator: Oscar Duque and Daniel Morinigo, Electrical Engineering Department laboratory, Universidad de Valladolid, Valladolid, Spain.
Choudhary, A. & Goyal, D. & Shimi, S. L. & Akula, A. (2019). Condition monitoring and fault diagnosis of induction motors: A review. Archives of Computational Methods in Engineering. In press. doi:10.1007/s11831-018-9286-z.
Garcia-Escudero, L. A., Duque-Perez, O., Fernandez-Temprano, M., Morinigo-Sotelo, D. (2016). Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts. IEEE Transactions on Industry Applications, 53(3), 3076-3085.
data(motors) summary(motors)data(motors) summary(motors)