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class BC extends AnyRef

Balance Cascade 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 BC(data: Data, seed: Long = System.currentTimeMillis(), dist: Distance = Distance.EUCLIDEAN, k: Int = 3, nMaxSubsets: Int = 5, nFolds: Int = 5, ratio: Double = 1.0, normalize: Boolean = false, randomData: Boolean = false, verbose: Boolean = false)

    data

    data to work with

    seed

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

    dist

    object of Distance enumeration representing the distance to be used

    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

    normalize

    normalize the data or not

    randomData

    iterate through the data randomly or not

    verbose

    choose to display information about the execution or not

Value Members

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  6. def compute(): Data

    Compute the BC algorithm.

    Compute the BC algorithm.

    returns

    undersampled data structure

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  1. def finalize(): Unit
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    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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