Documentation ¶
Overview ¶
Package lnsmote implement the Local-Neighborhood algorithm from the paper,
Maciejewski, Tomasz, and Jerzy Stefanowski. "Local neighbourhood extension of SMOTE for mining imbalanced data." Computational Intelligence and Data Mining (CIDM), 2011 IEEE Symposium on. IEEE, 2011.
Index ¶
Constants ¶
This section is empty.
Variables ¶
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var (
// DEBUG debug level, set from environment.
DEBUG = 0
)
Functions ¶
This section is empty.
Types ¶
type Runtime ¶ added in v0.2.0
type Runtime struct { // Runtime of SMOTE, since this module extend the SMOTE method. smote.Runtime // ClassMinor the minority sample in dataset that we want to // oversampling. ClassMinor string `json:"ClassMinor"` // OutliersFile if its not empty then outliers will be saved in file // specified by this option. OutliersFile string `json:"OutliersFile"` // contains filtered or unexported fields }
Runtime parameters for input and output.
func (*Runtime) Init ¶ added in v0.2.0
func (in *Runtime) Init(dataset tabula.ClasetInterface)
Init will initialize LNSmote runtime by checking input values and set it to default if not set or invalid.
func (*Runtime) Resampling ¶ added in v0.2.0
func (in *Runtime) Resampling(dataset tabula.ClasetInterface) ( e error, )
Resampling will run resampling process on dataset and return the synthetic samples.
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