Data Mining Lab Course WS 2017/18
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1 Data Mining Lab Course WS 2017/18 L. Richter Department of Computer Science Technische Universität München Wednesday, Dec 20th L. Richter DM Lab WS 17/18 1 / 14
2 L. Richter DM Lab WS 17/18 2 / 14
3 Table : Current Date Topic Date Topic Oct 18th Intro Dec 13th Descriptive Mining V Oct 25th Data Set Presentation Dec 20th Predictive Mining I Nov 1st Holiday Jan 10th Predictive Mining II Nov 8th Data Set Selection Jan 17th Predictive Mining III Nov 15th Descriptive Mining I Jan 24th Predictive Mining IV Nov 22th Descriptive Mining II Jan 31st Final Presentation Nov 29th Descriptive Mining III Feb 7th Final Presentation Dec 6th Descriptive Mining IV L. Richter DM Lab WS 17/18 3 / 14
4 Preprocessing Credits to Stefan Kramer for slide material L. Richter DM Lab WS 17/18 4 / 14
5 Dealing with numeric attributes: cannot be handled by the learning scheme performance is improved reduced overfitting the range of the feature is divided into a set of intervals Supervised vs Unsupervised: Supervised: consider the relation of the attribute values to the class values Unsupervised: only look at the distribution of values of the attribute L. Richter DM Lab WS 17/18 5 / 14
6 Unsupervised 1 Domain-dependent age: "baby" if in (0,3], "child" if in (3,6], "school child" if in (6,10], "teenager" if in (10,18] 2 Equal-width divide value range into a number of intervals of equal width 3 Equal-frequency divide value range into a number of intervals so that (approximately) the same number of data points are in each interval L. Richter DM Lab WS 17/18 6 / 14
7 Supervised 1 Entropy Split (Fayyad & Irani, 1993) splitting (top-down): starts with single interval and successively splits the interval into sub-intervals stops when a given number of intervals is reached or intervals become to small entropy as splitting criterion 2 ChiMerge (Kerber, 1992) merging (bottom-up): merging of adjacent intervals use χ 2 -statistics to determine pair of interval to be merged L. Richter DM Lab WS 17/18 7 / 14
8 Many features may be: irrelevant redundant Removing them can: increase efficiency improve accuracy prevent overfitting Feature (subset) selection techniques try to determine appropriate features automatically L. Richter DM Lab WS 17/18 8 / 14
9 Unsupervised Using domain knowledge: Some features may be known to be irrelevant or redundant, common sense Random Sampling: select a random sample from the features may be appropriate in case of many weakly relevant features or in connected with so-called ensemble methods L. Richter DM Lab WS 17/18 9 / 14
10 Supervised Filter Approaches: using some evaluation measure of attribute with respect to class Wrapper Approaches: using learning algorithm as plug-in to evaluate feature set(s) L. Richter DM Lab WS 17/18 10 / 14
11 Feature Measures for Filters Gini-Index: describes how a given attribute supports the partition of a set of instances into two subsets with respect to the class label (0 no class separation at all, 1 perfectly separated classes) Information Gain: calculates the entropy reduction for the split in a given attribute Relief: determines attribute weights for best separation by distance to nearhit and nearmiss instance L. Richter DM Lab WS 17/18 11 / 14
12 Wrappers Search through the space of possible feature subsets Each subset encountered in search is tried with a learning algorithm Error rate in cross-validation as evaluation function improve it by modifying the feature subset based on the result L. Richter DM Lab WS 17/18 12 / 14
13 Pro s and Con s Disadvantage: very inefficient for certain learning schemes: many cycles necessary higher risk of overfitting Advantage: feature subset is tailored to the learning algorithm can consider combination of features can eliminate redundant features L. Richter DM Lab WS 17/18 13 / 14
14 Strategies Forward Selection: start with trying a single feature select and add feature with the best performance new iteration to get the next best feature terminates upon fixed number of features or plateau Backward Selection: starts with full feature set search for attribute with the least loss of performance new iteration to get next elimination candidate L. Richter DM Lab WS 17/18 14 / 14
15 Questions? L. Richter DM Lab WS 17/18 15 / 14
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