class NearMiss extends Algorithm

NearMiss. Original paper: "kNN Approach to Unbalanced Data Distribution: A Case Study involving Information Extraction" by Jianping Zhang and Inderjeet Mani.

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Instance Constructors

  1. new NearMiss(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, version: Int = 1, nNeighbours: Int = 3, ratio: Double = 1.0): Data

    Compute NearMiss undersampling (NM rule)

    Compute NearMiss undersampling (NM rule)

    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

    version

    version of the algorithm to execute

    nNeighbours

    number of neighbours to take for each minority example (only used if version is set to 3)

    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

    Data structure with all the important information

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    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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