Description Usage Arguments Details Value Author(s) References See Also Examples

This function performs an adapted one-sided selection strategy for multiclass imbalanced problems.

1 | ```
OSSClassif(form, dat, dist = "Euclidean", p = 2, Cl = "smaller", start = "CNN")
``` |

`form` |
A formula describing the prediction problem. |

`dat` |
A data frame containing the original imbalanced data set. |

`dist` |
A character string indicating which distance metric to use when determining the k nearest neighbors. See the details. Defaults to "Euclidean". |

`p` |
A number indicating the value of p if the "p-norm" distance is chosen. Only necessary to define if a "p-norm" is chosen in the |

`Cl` |
A character vector indicating which are the most important classes. Defaults to "smaller" which means that the smaller classes are automatically determined. In this case, all the smaller classes are those with a frequency below (nr.examples)/(nr.classes). With the selection of option "smaller" those classes are the ones considered important for the user. |

`start` |
A string which determines which strategy (CNN or Tomek links) should be performed first. The existing options are "CNN" and "Tomek". The first one, "CNN", which is the default, means that CNN strategy will be performed first and Tomek links are applied after. On the other hand, if |

`dist`

parameter:The parameter

`dist`

allows the user to define the distance metric to be used in the neighbors computation. Although the default is the Euclidean distance, other metrics are available. This allows the computation of distances in data sets with, for instance, both nominal and numeric features. The options available for the distance functions are as follows:- for data with only numeric features: "Manhattan", "Euclidean", "Canberra", "Chebyshev", "p-norm";

- for data with only nominal features: "Overlap";

- for dealing with both nominal and numeric features: "HEOM", "HVDM".

When the "p-norm" is selected for the

`dist`

parameter, it is also necessary to define the value of parameter`p`

. The value of parameter`p`

sets which "p-norm" will be used. For instance, if`p`

is set to 1, the "1-norm" (or Manhattan distance) is used, and if`p`

is set to 2, the "2-norm" (or Euclidean distance) is applied. For more details regarding the distance functions implemented in UBL package please see the package vignettes.

The function returns a data frame with the new data set resulting from the application of the selected OSS strategy.

Paula Branco [email protected], Rita Ribeiro [email protected] and Luis Torgo [email protected]

Kubat, M. & Matwin, S. (1997). *Addressing the Curse of Imbalanced Training Sets: One-Sided Selection* Proc. of the 14th Int. Conf. on Machine Learning, Morgan Kaufmann, 179-186.

Batista, G. E.; Prati, R. C. & Monard, M. C. (2004). *A study of the behavior of several methods for balancing machine learning training data* ACM SIGKDD Explorations Newsletter, ACM, 6, 20-29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Not run:
library(DMwR)
data(algae)
clean.algae <- algae[complete.cases(algae), ]
alg1 <- OSSClassif(season~., clean.algae, dist = "HVDM",
Cl = c("spring", "summer"))
alg2 <- OSSClassif(season~., clean.algae, dist = "HEOM",
Cl = c("spring", "summer"), start = "Tomek")
alg3 <- OSSClassif(season~., clean.algae, dist = "HVDM", start = "CNN")
alg4 <- OSSClassif(season~., clean.algae, dist = "HVDM", start = "Tomek")
alg5 <- OSSClassif(season~., clean.algae, dist = "HEOM", Cl = "winter")
summary(alg1$season)
summary(alg2$season)
summary(alg3$season)
summary(alg4$season)
summary(alg5$season)
## End(Not run)
``` |

```
Loading required package: MBA
Loading required package: gstat
Loading required package: automap
Loading required package: sp
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: lattice
Loading required package: grid
autumn spring summer winter
22 48 43 34
autumn spring summer winter
19 48 43 32
autumn spring summer winter
36 30 43 36
autumn spring summer winter
36 29 43 34
autumn spring summer winter
19 29 26 57
```

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