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Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions Kun Zhang, Wei Fan, Xiaojing Yuan, Ian Davidson, and Xiangshang Li Recall Precision Ma Mb VEVE

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What this Paper Offers Application: more accurate (higher recall & precision) solution to predict ozone days Interesting and Difficult Data Mining Problem: High dimensionality and some could be irrelevant features: 72 continuous, 10 verified by scientists to be relevant Skewed class distribution : either 2 or 5% ozone days depending on ozone day criteria (either 1-hr peak and 8-hr peak) Streaming: data in the past collected to train model to predict the future. Feature sample selection bias: hard to find many days in the training data that is very similar to a day in the future Stochastic true model: given measurable information, sometimes target event happens and sometimes it doesnt.

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Key Solution Highlights Non-parametric models are easier to use when physical or generative mechanism is unknown. Reliable conditional probabilities estimation under skewed, high-dimensional, possibly irrelevant features, … Estimate decision threshold predict the unknown distribution of the future

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Seriousness of Ozone Problem Ground ozone level is a sophisticated chemical and physical process and stochastic in nature. Ozone level above some threshold is rather harmful to human health and our daily life.

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Drawbacks of current ozone forecasting systems Traditional simulation systems Consume high computational power Customized for a particular location, so solutions not portable to different places Regression-based methods E.g. Regression trees, parametric regression equations, and ANN Limited prediction performances

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Ozone Level Prediction: Problems we are facing Daily summary maps of two datasets from Texas Commission on Environmental Quality (TCEQ)

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1. Rather skewed and relatively sparse distribution 2500+ examples over 7 years (1998-2004) 72 continuous features with missing values Huge instance space If binary and uncorrelated, 2 72 is an astronomical number 2% and 5% true positive ozone days for 1-hour and 8-hour peak respectively Challenges as a Data Mining Problem

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2. True model for ozone days are stochastic in nature. Given all relevant features X R, P(Y = ozone day| X R ) < 1 Predictive mistakes are inevitable

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3. A large number of irrelevant features Only about 10 out of 72 features verified to be relevant, No information on the relevancy of the other 62 features For stochastic problem, given irrelevant features X ir, where X=(X r, X ir ), P(Y|X) = P(Y|X r ) only if the data is exhaustive. May introduce overfitting problem, and change the probability distribution represented in the data. P(Y = ozone day| X r, X ir ) 1 P(Y = normal day| X r, X ir ) 0

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4. Feature sample selection bias. Given 7 years of data and 72 continuous features, hard to find many days in the training data that is very similar to a day in the future Given these, 2 closely-related challenges 1. How to train an accurate model 2. How to effectively use a model to predict the future with a different and yet unknown distribution Training Distribution Testing Distribution 1 2 3 1 2 3 + + + + + + - -

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Addressing Challenges Skewed and stochastic distribution Probability distribution estimation Parametric methods Non-parametric methods Decision threshold determination through optimization of some given criteria Compromise between precision and recall List of methods: Logistic Regression Naïve Bayes Kernel Methods Linear Regression RBF Gaussian mixture models List of methods: Decision Trees RIPPER rule learner CBA: association rule clustering-based methods … … Recall Precision Ma Mb Highly accurate if the data is indeed generated from that model you use! But how about, you dont know which to choose or use the wrong one? use a family of free-form functions to match the data given some preference criteria. free form function/criteria is appropriate. preference criteria is appropriates VEVE

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Reliable probability estimation under irrelevant features Recall that due to irrelevant features: P(Y = ozone day| X r, X ir ) 1 P(Y = normal day| X r, X ir ) 0 Construct multiple models Average their predictions P(ozone|x r ): true probability P(ozone| X r, X ir, θ ): estimated probability by model θ MSE singlemodel: Difference between true and estimated. MSE Average Difference between true and average of many models Formally show that MSE Average MSE SingleModel

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Prediction with feature sample selection bias 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 7/1/98 0.1316 Normal 7/2/98 0.6245 Ozone 7/3/98 0.5944 Ozone ……… PrecRec plot Recall Precision Ma Mb A CV based procedure for decision threshold selection Training Distribution Testing Distribution 1 2 3 1 2 3 + + + + + + - - P(y=ozoneday|x,θ) Lable 7/1/98 0.1316 Normal 7/3/98 0.5944 Ozone 7/2/98 0.6245 Ozone ………

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Addressing Data Mining Challenges Prediction with feature sample selection bias Future prediction based on decision threshold selected Whole Training Set θ Classification on future days if P(Y = ozonedays|X,θ ) V E Predict ozonedays

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Probabilistic Tree Models Single tree estimators C4.5 (Quinlan93) C4.5Up,C4.5P C4.4 (Provost03) Ensembles RDT (Fan et al03) Member tree trained randomly Average probability Bagging Probabilistic Tree (Breiman96) Bootstrap Compute probability Member tree: C4.5, C4.4 RDT: Random Decision Tree (Fan et al03) Encoding data in trees. At each node, an un-used feature is chosen randomly A discrete feature is un-used if it has never been chosen previously on a given decision path starting from the root to the current node. A continuous feature can be chosen multiple times on the same decision path, but each time a different threshold value is chosen Stop when one of the following happens: A node becomes too small (<= 3 examples). Or the total height of the tree exceeds some limits: Different from Random Forest 1.Original Data vs Bootstrap 2.Random pick vs. Random Subset + info gain 3.Probability Averaging vs. Voting

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Optimal Decision Boundary from Tony Lius thesis (supervised by Kai Ming Ting)

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Baseline Forecasting Parametric Model in which, O3 - Local ozone peak prediction Upwind - Upwind ozone background level EmFactor - Precursor emissions related factor Tmax - Maximum temperature in degrees F Tb - Base temperature where net ozone production begins (50 F) SRd - Solar radiation total for the day WSa - Wind speed near sunrise (using 09-12 UTC forecast mode) WSp - Wind speed mid-day (using 15-21 UTC forecast mode)

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Model evaluation criteria Precision and Recall At the same recall level, M a is preferred over M b if the precision of M a is consistently higher than that of M b Coverage under PR curve, like AUC Recall Precision

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Some Coverage Results 8-hour: recall = [0.4,0.6] Coverage under PR-Curve

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Some Action Results Annual test 1.BC4.4 and RDT more accurate than baseline Para 2.BC4.4 and RDT less surprise than single tree 1.Previous years data for training 2.Next year for testing 3.Repeated 6 times using 7 years of data 1.C4.4 best among single trees 2.BC4.4 and RDT best among tree ensembles 8-hour: thresholds selected at the recall = 0.6 1-hour: thresholds selected at the recall = 0.6 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 BC4.4RDTC4.4Para Recall Precision

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Summary Procedures to formulate as a data mining problem, Analysis of combination of technical challenges Process to search for the most suitable solutions. Model averaging of probability estimators can effectively approximate the true probability A lot of irrelevant features Feature sample selection bias A CV based guide for decision threshold determination for stochastic problems under sample selection bias

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AUC Score Given dataset Signal-noise separability estimation through RDT or BPET Ensembl e or Single trees Low signal-noise separability High signal- noise separability Ensemble or Single trees Ensemble (AUC,MSE, ErrorRate) RDT CFT Single Trees (AUC,MSE, ErrorRate) >=0.9 < 0.9 Ensemble Single Tree AUC MSE Error Rate CFT AUC MSE, ErrorRate C4.5 or C4.4 Feature types and value characteristic s Categorical feature with limited values BPET RDT ( BPET) Continuous features or categorical feature with a large number of values AUC, MSE, ErrorRate Choosing the Appropriate PET come to our other talk 10:30 RM 402

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Thank you! Questions?

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