Big Data Workshop
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This document discusses techniques for dealing with large datasets, including attribute selection, attribute discretization, and data transformation. It provides examples of how different attribute selection methods like backward selection and filtering can result in a different number of selected attributes. It also compares different discretization techniques like Chi-2 based and top-down discretization. Finally, it discusses data transformation methods like principal component analysis and projection pursuit regression.
Big Data Workshop
- 1. Dealing with large datasets Avoiding the dangers Adrien Ickowicz, Ross Sparks MATHEMATICS, INFORMATICS AND STATISTICS www.csiro.au
- 2. Managing the data Can the input be massaged to make it more amenable for learning methods? (and how can you do it safely) Attribute Selection Attribute Discretization – Scheme independent selection – Unsupervized discretization – Searching the attribute space – Entropy-based discretization – Scheme specific selection – Other methods Data Transformation Data Cleansing – Linear and Non-linear PCA – Improving Decision Tree – Random projections – Robust Regression – Time Series – Detecting anomalies Dealing with large datasets: Slide 2 of 17
- 3. Attribute Selection Ju st ifi ca An irrelevant attribute will often distract the performance tio of state-of-the-art decision tree and rule learners... n ¯ Example: Random binary attribute – Deteriorates the classification performance 5% to 10% of the time But a relevant attribute can be harmful as well... ¯ Example: 65% same-class-value binary attribute – Deteriorates the classification performance 1% to 5% of the time Dealing with large datasets: Slide 3 of 17
- 4. Attribute Selection 1 - Scheme-independant selection • No universal relevance measure • Beware of overfitting and model redundancy • Make sure that the attributes scales are the same 2 - Searching the attribute space • Exhaustive search impractical • Forward, backward, ... : Need an expert to set alg. param. 3 - Scheme-specific selection • Time consuming • ”Burns” one classification method Dealing with large datasets: Slide 4 of 17
- 5. Attribute Discretization Ju st ifi ca Deal with both continuous and discretized data tio n Handle the extreme values Some algorithms assume a unrealistic hypothesis on the attribute values... ¯ Example: normal distribution assumption ... or slow down the process. ¯ Example: need to sort the attribute values Dealing with large datasets: Slide 5 of 17
- 6. Attribute Discretization 1 - Unsupervized discretization • Avoid big differences in bin-frequencies • Avoid small sized bins 2 - Entropy-based discretization • Recursive, so need a stopping criterion 3 - Other methods • In practice, do not perform better than E-B-D. • Some are time consuming Dealing with large datasets: Slide 6 of 17
- 7. Data Transformation Ju st ifi ca Data often calls for general mathematical transforma- tio tions of a set of attributes... n ¯ Example: Two date attributes may lead to a third attribute representing age Test the robustness of a learning algorithm... ¯ Example: add noise or change a given percentage of a nom- inal attribute values Dealing with large datasets: Slide 7 of 17
- 8. Data Transformation 1 - Linear and Non-linear PCA • Dimension reduction technique: there is a loose in information • Very costly in high dimension 2 - Random projections • Perform worse than PCA • Preserve distance relationship well on average 3 - Time Series • Pay attention to the sampling Dealing with large datasets: Slide 8 of 17
- 9. Application Example - What is the difference between theory and practice? - There is no difference ... in theory. But in practice, there is. ¯ Example 1: Attribute Selection (Backward vs Filter) ¯ Example 2: Attribute Discretization (Chi-2 based vs Top-down) ¯ Example 3: Data Transformation Dealing with large datasets: Slide 9 of 17
- 10. Example 1 Data Set : Wine quality Data Description of the data: 1599 obs. of 12 variables Question : What makes a good (red) wine? Dealing with large datasets: Slide 10 of 17
- 11. Example 1 How many features do we keep? Backward RMSE Number of features: 5 Dealing with large datasets: Slide 11 of 17
- 12. Example 1 How many features do we keep? Filter RMSE Dealing with large datasets: Slide 12 of 17
- 13. Example 2 How do we discretize the features? Chi-2 discretization MDL discretization Dealing with large datasets: Slide 13 of 17
- 14. Example 2 How do we discretize the features? Chi-2 Merge discretization Top-down discretization Dealing with large datasets: Slide 14 of 17
- 15. Example 3 How do we transform the data? Principal Component Analysis Dealing with large datasets: Slide 15 of 17
- 16. Example 3 How do we transform the data? Projection Pursuit Regression Dealing with large datasets: Slide 16 of 17
- 17. CSIRO Mathematics, Informatics and Statistics CSIRO Mathematics, Informatics and Statistics Adrien Ickowicz Ross Sparks t +61 2 9325 3260 t +61 2 9325 3262 e Adrien.Ickowicz@csiro.au e Ross.Sparks@csiro.au w Mathematics, Informatics and Statistics web w Mathematics, Informatics and Statistics web MATHEMATICS, INFORMATICS AND STATISTICS www.csiro.au