c

undersampling.core

BalanceCascade

class BalanceCascade extends Algorithm

Easy Ensemble algorithm. Original paper: "Exploratory Undersampling for Class-Imbalance Learning" by Xu-Ying Liu, Jianxin Wu and Zhi-Hua Zhou.

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Instance Constructors

  1. new BalanceCascade(data: Data, seed: Long = System.currentTimeMillis(), minorityClass: Any = -1)

    data

    data to work with

    seed

    seed to use. If it is not provided, it will use the system time

    minorityClass

    indicates the minority class. If it's set to -1, it will set to the one with less instances

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  14. def sample(file: Option[String] = None, distance: Distance = Distances.EUCLIDEAN, k: Int = 3, nMaxSubsets: Int = 5, nFolds: Int = 5, ratio: Double = 1.0): Data

    Compute the Balance Cascade algorithm.

    Compute the Balance Cascade algorithm.

    file

    file to store the log. If its set to None, log process would not be done

    distance

    distance to use when calling the NNRule algorithm

    k

    number of neighbours to use when computing k-NN rule (normally 3 neighbours)

    nMaxSubsets

    maximum number of subsets to generate

    nFolds

    number of subsets to create when applying cross-validation

    ratio

    ratio to know how many majority class examples to preserve. By default it's set to 1 so there will be the same minority class examples as majority class examples. It will take numMinorityInstances * ratio

    returns

    array of Data structures with all the important information and index of elements kept for each subset

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    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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