# Binary classification with Kernel Factory

### Description

`kernelFactory`

implements an ensemble method for kernel machines (Ballings and Van den Poel, 2013).

### Usage

1 2 3 4 |

### Arguments

`x` |
A data frame of predictors (numeric, integer or factor). Categorical variables need to be factors. Indicator values should not be too imbalanced because this might produce constants in the subsetting process. |

`y` |
A factor containing the response vector. Only {0,1} is allowed. |

`cp` |
The number of column partitions. |

`rp` |
The number of row partitions. |

`method` |
Can be one of the following: POLynomial kernel function ( |

`ntree` |
Number of trees in the Random Forest base classifiers. |

`filter` |
either NULL (deactivate) or a percentage denoting the minimum class size of dummy predictors. This parameter is used to remove near constants. For example if nrow(xTRAIN)=100, and filter=0.01 then all dummy predictors with any class size equal to 1 will be removed. Set this higher (e.g., 0.05 or 0.10) in case of errors. |

`popSize` |
Population size of the genetic algorithm. |

`iters` |
Number of generations of the genetic algorithm. |

`mutationChance` |
Mutationchance of the genetic algorithm. |

`elitism` |
Elitism parameter of the genetic algorithm. |

`oversample` |
Oversample the smallest class. This helps avoid problems related to the subsetting procedure (e.g., if rp is too high). |

### Value

An object of class `kernelFactory`

, which is a list with the following elements:

`trn` |
Training data set. |

`trnlst` |
List of training partitions. |

`rbfstre` |
List of used kernel functions. |

`rbfmtrX` |
List of augmented kernel matrices. |

`rsltsKF` |
List of models. |

`cpr` |
Number of column partitions. |

`rpr` |
Number of row partitions. |

`cntr` |
Number of partitions. |

`wghts` |
Weights of the ensemble members. |

`nmDtrn` |
Vector indicating the numeric (and integer) features. |

`rngs` |
Ranges of numeric predictors. |

`constants` |
To exclude from newdata. |

### Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com

### References

Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913.

### See Also

`predict.kernelFactory`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
#Credit Approval data available at UCI Machine Learning Repository
data(Credit)
#take subset (for the purpose of a quick example) and train and test
Credit <- Credit[1:100,]
train.ind <- sample(nrow(Credit),round(0.5*nrow(Credit)))
#Train Kernel Factory on training data
kFmodel <- kernelFactory(x=Credit[train.ind,names(Credit)!= "Response"],
y=Credit[train.ind,"Response"], method=random)
#Deploy Kernel Factory to predict response for test data
#predictedresponse <- predict(kFmodel, newdata=Credit[-train.ind,names(Credit)!= "Response"])
``` |