Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions Presentor: Prof. Longbin Cao Wei Fan, Kun Zhang, and Xiaojing Yuan Recall Precision Ma Mb VEVE

What is the business problem and broad- based areas Problem: ozone pollution day detection Ground ozone level is a sophisticated chemical, physical process and “stochastic” in nature. Ozone level above some threshold is rather harmful to human health and our daily life. 8-hour peak and 1-hour peak standards. 8-hour average > 80 ppt (parts per billion) 1-hour average > 120 ppt It happens from 5 to 15 days per year. Broad-area: Environmental Pollution Detection and Protection Drawback of alternative approaches Simulation: c onsume high computational power; customized for a particular location, so solutions not portable to different places Physical model approach: hard to come up with good equations when there are many parameters, and changes from place to place

What are the research challenges that cannot be handled by the state-of-the-art? Dataset is sparse, skewed, stochastic, biased and streaming in the same time. High dimensional Very few positives Under similar conditions: sometimes it happens and sometimes it doesn’t P(x) difference between training and testing Training data from past, predicting the future Physical model is not well understood and cannot be customized easily from location to location

what is the main idea of your approach? Non-parametric models are easier to use when “physical or generative mechanism” is unknown. Reliable “conditional probabilities” estimation under “skewed, biased, high-dimensional, possibly irrelevant features Estimate “decision threshold” to predict on the unknown distribution of the future Random Decision Tree Super fast implementation Formal Analysis: Bound analysis MSE reduction Bias and bias reduction P(y|x) order correctness proof

TrainingSet Algorithm ….. Estimated probability values 1 fold Estimated probability values 10 fold 10CV Estimated probability values 2 fold Decision threshold V E VEVE “Probability- TrueLabel” file Concatenate P(y=“ozoneday”|x,θ) Lable 7/1/ Normal 7/2/ Ozone 7/3/ Ozone ……… PrecRec plot Recall Precision Ma Mb A CV based procedure for decision threshold selection Training Distribution Testing Distribution P(y=“ozoneday”|x,θ) Lable 7/1/ Normal 7/3/ Ozone 7/2/ Ozone ……… Decision Threshold when P(x) is different and P(y|x) is non-deterministic

Random Decision Tree B1: {0,1} B2: {0,1} B3: continuous B2: {0,1} B3: continuous B2: {0,1} B3: continuous B2: {0,1} B3: continous Random threshold 0.3 Random threshold 0.6 B1 chosen randomly B2 chosen randomly B3 chosen randomly RDT vs Random Forest 1.Original Data vs Bootstrap 2.Random pick vs. Random Subset + info gain 3.Probability Averaging vs. Voting 4.RDT: superfast

Optimal Decision Boundary from Tony Liu’s thesis (supervised by Kai Ming Ting)

what is the main advantage of your approach, how do you evaluate it? Fast and Reliable Compare with State-of-the-art data mining algorithms: Decision tree NB Logistic Regression SVM (linear and RBF kernel) Boosted NB and Decision Tree Bagging Random Forest Physical Equation-based Model Actual streaming environment on daily basis

what impact has been made in particular, changing the real world business? From 4-year studies on actual data, the proposed data mining approach consistently outperforms physical model-based method

can your approach be widely expanded to other areas? and how easy would it be? Other known application using proposed approach Fraud Detection Manufacturing Process Control Congestion Prediction Marketing Social Tagging Proposed method is general enough and doesn’t need any tuning or re-configuration