【 ワン・クラス・サポートベクターマシン (one-class-SVM)で簡単な異常検知】

[@EastWood19802]

◎Rのプログラム
# 1. ライブラリの読み込み
library(kernlab)
# 2. 乱数のシードを設定
set.seed(123)
# 3. ランダムデータを生成
x = rnorm(1000)
y = rnorm(1000)
data = data.frame(type = 1, x, y)
# 4. One-Class SVMモデルを作成
one_class_SVM = ksvm(type ~ ., data = data,
type = "one-svc", kernel = "rbfdot",
kpar = list(sigma = 0.1), cross = 10, nu = 0.1)
# 5. 正常値と外れ値を予測
pre = predict(one_class_SVM)
# 6. 予測結果の変換
c.pre = ifelse(pre == TRUE, 1, 2)
# 7. 予測結果の結合
data.result = cbind(data, c.pre)
# 8. 結果のプロット：正常値は青、外れ値は赤、外れ値は×で表示
plot(data.result[, 2:3],
pch = ifelse(data.result$c.pre == 1, 21, 4),
bg = ifelse(data.result$c.pre == 1, "blue", "red"),
col = ifelse(data.result$c.pre == 1, "blue", "red"),
cex = ifelse(data.result$c.pre == 1, 1, 1.5),
lwd = ifelse(data.result$c.pre == 1, 1, 2),
xlab = "X 値", ylab = "Y 値",
main = "One-Class SVMによる異常検知",
col.main = "blue", col.lab = "blue", col.axis = "blue")

legend("topright", legend = c("正常値", "外れ値"),
col = c("blue", "red"),
pch = c(21, 4),
pt.bg = c("blue", "red"),
pt.cex = c(1, 1.5),
lwd = c(1, 2))