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Kaggle-Indoor solution

Kaggle-Indoor solution

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Kyohei Uto

May 19, 2021
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  1. Indoor Location & Navigation Dataset Post Processing (PP) Training (2

    stage) Hidden waypoints by LI Hidden waypoints by KF - Make wifi-based dataset both train & test - Remove bssid if time-diff (waypoint and last-seen timestamp) is more 10s - Minimum number of wifi=7 - Interpolate hidden waypoints by Linear Interpolation(LI) and Kalman Filter(KF) - Calculate timediff (between waypoint and wifi group) BSSID RSSI Site id Floor Embedding Embedding xy floor CustomLoss MESLoss dim=64 dim=64 FC layer1(128→256) LSTM layer×2(256→128→16) FC layer2(xy:16→2, floor:16→1) 1st stage 2nd stage oof pred Add test data with pseudo labeling Remove train data if oof’s error is over 40m Loss - MSELoss-based - Given weight according to timediff - if timediff is large, weight become smaller (don’t learn too much) Ensemble Cost minimaization Snap to grid Device id leakage Repeats 6 times Repeated PP ▪ Snap to grid ▪ Cost minimaization 80 pieces Delta correction by linear regression using sensor delta and target delta Automatically generate multiple patterns of extra grid. mean by timestamp Team: EXODIA REBORN at MOTOSUMIYOSHI Given waypoints Replace the predicted value of floor with the predicted value of another model (lightGBM and BiLSTM). Copyright 2021 @kuto_bopro 4 pattern grid when do snap to grid ❶ snap to grid’s threshold=None / sparse extra grid ❷ snap to grid’s threshold=None / dense extra grid ❸ snap to grid’s threshold=None / edge extra grid ❹ snap to grid’s threshold=5 / only train grid model1 repeated pp by 4 pattern grid (❶〜❹) model2 model3 stacking light GBM final submission weighted mean ×3 ×3 Private LB: 16th (3.562)