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異常検知の評価指標って何を使えばいいの? / Metrics for one-class classification

Kon
October 19, 2018

異常検知の評価指標って何を使えばいいの? / Metrics for one-class classification

Kon

October 19, 2018
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  1. Yu Ohori (a.k.a. Kon) NS Solutions Corporation (Apr 2017 -

    ) • Researcher • Data Science & Infrastructure Technologies • System Research & Development Center • Technology Bureau @Y_oHr_N @Y-oHr-N #SemiSupervisedLearning #AnomalyDetection #DataOps
  2. 不均衡データの場合,評価指標に F 値を使う事が多い 適合率(precision)と 再現率(recall)の調和平均で表される評価指標 実ラベル Y 混同行列 (confusion matrix)

    正常 pos: +1 異常 neg: -1 予測ラベル f(X) 正常 pos: +1 true positive (tp) false positive (fp) 異常 neg: -1 false negative (fn) true negative (tn) 4
  3. F 値に似た Lee-Liu metric と呼ばれる評価指標がある 適合率と再現率の幾何平均の二乗の定数倍で表される 評価指標 実ラベル Y 混同行列

    (confusion matrix) 正常 pos: +1 異常 neg: -1 予測ラベル f(X) 正常 pos: +1 true positive (tp) false positive (fp) 異常 neg: -1 false negative (fn) true negative (tn) Lee, W. S, and Liu, B., "Learning with positive and unlabeled examples using weighted Logistic Regression," In Proceedings of ICML, pp. 448-455, 2003. 6