a2htray / go-relief

Relief family for feature selection

Relief Family for Feature Selection within Golang

Table of Content

• Installation
• Usage
• Reference

Installation

go get github.com/a2htray/relief

Usage

Relief

package main

import (
       	"fmt"
       	"github.com/a2htray/relief"
)

func main() {
	model := relief.NewRelief([][]float64{
        []float64{0, 0},
        []float64{0, 1},
        []float64{1, 0},
        []float64{1, 1},
        []float64{1, 1},
    }, []float64{0, 0, 0, 1, 1}, []int{
        relief.AttributeTypeDiscrete,
        relief.AttributeTypeDiscrete,
    })
    fmt.Println(model.Run(20))
}

[-0.6 0.1]

From the output, we confirm that the second feature significantly contributes to the labels in all dataset.

RelifF

package main

import (
       	"fmt"
       	"github.com/a2htray/relief"
)

func main() {
	model := relief.NewReliefF([][]float64{
    		[]float64{0, 0, -1},
    		[]float64{0, 0, -2},
    		[]float64{0, 0, -3},
    		[]float64{0, 0, -4},
    		[]float64{1, 1, 1},
    		[]float64{1, 1, 2},
    		[]float64{1, 1, 3},
    		[]float64{1, 1, 4},
    	}, []float64{0, 0, 0, 0, 1, 1, 1, 1}, []int{
    		relief.AttributeTypeDiscrete,
    		relief.AttributeTypeDiscrete,
    		relief.AttributeTypeDiscrete,
    	}, 2)
    	fmt.Println(model.Run(100))
}

[-1.0000000000000007 -1.0000000000000007 0]

From the output, we should take the third feature into learning model, like SVM, NN, etc.

Reference

• Robnik-Šikonja, Marko, and Igor Kononenko. "An adaptation of Relief for attribute estimation in regression." Machine Learning: Proceedings of the Fourteenth International Conference (ICML’97). Vol. 5. 1997.