GhostMiner Wine example

1. GhostMiner Wine example Włodzisław Duch Dept. of Informatics, Nicholas Copernicus University, Toruń, Poland http://www.phys.uni.torun.pl/~duch ISEP Porto, 8-12 July 2002

2. GhostMiner Philosophy • GhostMiner, data mining tools from our lab. http://www.fqspl.com.pl/ghostminer/ • Separate the process of model building and knowledge discovery from model use => GhostMiner Developer & GhostMiner Analyzer. • There is no free lunch – provide different type of tools for knowledge discovery. Decision tree, neural, neurofuzzy, similarity-based, committees. • Provide tools for visualization of data. • Support the process of knowledge discovery/model building and evaluating, organizing it into projects.

3. GM summary Ghost Miner combines 4 basic tools for predictive data mining and understanding of data, avoiding too many choices of parameters (like network structure specs): • IncNet ontogenic neural network using Kalman filter learning separating each class from all other classes; • Feature Space Mapping neurofuzzy system producing logical rules of crisp and fuzzy types. • Separability Split Value decision tree. • Weighted nearest neighbor method. • K-classifiers and committees of models. • MDS visualization

4. Wine data example Chemical analysis of wine from grapes grown in the same region in Italy, but derived from three different cultivars.Task: recognize the source of wine sample.13 quantities measured, continuous features: • alcohol content • ash content • magnesium content • flavanoids content • proanthocyanins phenols content • OD280/D315 of diluted wines • malic acid content • alkalinity of ash • total phenols content • nonanthocyanins phenols content • color intensity • hue • proline.

5. Exploration and visualization Load data (using load icon) and look at general info about the data.

6. Exploration: data Inspect the data itself in the raw form.

7. Exploration: data statistics Look at distribution of feature values Note that Proline has very large values, therefore the data should be standardized before further processing.

8. Exploration: data standardized Standardized data: unit standard deviation, about 2/3 of all data should fall within [mean-std,mean+std] Other options: normalize to fit in[-1,+1], or normalize rejecting some extreme values.

9. Exploration: 1D histograms Distribution of feature values in classes Some features are more useful than the others.

10. Exploration: 1D/3D histograms Distribution of feature values in classes, 3D

11. Exploration: 2D projections Projections (cuboids) on selected 2D Projections on selected 2D

12. Visualize data Relations in more than 3D are hard to imagine. SOM mappings: popular for visualization, but rather inaccurate, no measure of distortions. Measure of topographical distortions: map all Xipoints from Rnto xipoints inRm, m < n, and ask: How well are Rij = D(Xi,Xj) distances reproduced by distances rij = d(xi,xj) ?Use m = 2 for visualization, use higher m for dimensionality reduction.

13. Visualize data: MDS Multidimensional scaling: invented in psychometry by Torgerson (1952), re-invented by Sammon (1969) and myself (1994) … Minimize measure of topographical distortions moving the x coordinates.

14. Visualize data: Wine 3 clusters are clearly distinguished, 2D is fine. The green outlier can be identified easily.

15. Decision trees Simplest things first: use decision tree to find logical rules. Test single attribute, find good point to split the data, separating vectors from different classes. DT advantages: fast, simple, easy to understand, easy to program, many good algorithms. 4 attributes used, 10 errors, 168 correct, 94.4% correct.

16. Decision borders Univariate trees: test the value of a single attribute x < a. Multivariate trees: test on combinations of attributes, hyperplanes. Result: feature space is divided into cuboids. Wine data: univariate decision tree borders for proline and flavanoids

17. Separability Split Value (SSV) • SSV criterion: • select attribute and split value that maximizes the number of correctly separated pairs from different classes; • if several equivalent split values exist select one that minimizes the number of pairs split from the same class. • Works on raw data, including symbolic values. • Search for splits using best-first or beam-search method. • Tests are A(x) < T or x  {si} • Create tree that classifies all data correctly. • Use crossvalidation to determine how many node to prune or what should be the pruning level.

18. Wine – SSV 5 rules Lower pruning leads to more complex tree. 7 nodes, corresponding to 5 rules; 10 errors, mostly Class2/3 wines mixed; check the confusion matrix in “results”.

19. Wine – SSV optimal rules What is the optimal complexity of rules? Use crossvalidation to estimate generalization. Various solutions may be found, depending on the search: 5 rules with 12 premises, making 6 errors, 6 rules with 16 premises and 3 errors, 8 rules, 25 premises, and 1 error. if OD280/D315 > 2.505  proline > 726.5  color > 3.435 then class 1 if OD280/D315 > 2.505  proline > 726.5  color < 3.435 then class 2 if OD280/D315 < 2.505  hue > 0.875  malic-acid < 2.82 then class 2 if OD280/D315 > 2.505  proline < 726.5 then class 2 if OD280/D315 < 2.505  hue < 0.875 then class 3 if OD280/D315 < 2.505  hue > 0.875  malic-acid > 2.82 then class 3

20. Neurofuzzy systems MLP: discrimination, finds separating surfaces as combination of sigmoidal functions. Fuzzy approach: define MF replacing m(x)=0,1 (no/yes) by a degree m(x)[0,1]. Typically triangular, trapezoidal, Gaussian ...MF are used. M.f-s in many dimensions are constructed using products to determine the threshold of m(X)=const[0,1]. Advantage: easy to add a priori knowledge (proper bias); may work well for very small datasets!

21. Feature Space Mapping Feature Space Mapping (FSM) neurofuzzy system. Find best network architecture (number of nodes and feature selection) using an ontogenic network (growing and shrinking) with one hidden layer. Use separable rectangular, triangular, Gaussian MF. Describe the joint prob. density p(X,C). Neural adaptation using RBF-like algorithms. Good for logical rules and NN predictive models. Initialize using clusterization techniques. Allow for rotation of Gaussian functions.

22. Wine – FSM rules SSV: hierarchical rules FSM: density estimation with feature selection. Complexity of rules depends on desired accuracy. Use rectangular functions for crisp rules. Optimal accuracy may be evaluated using crossvalidation. FSM discovers simpler rules, for example: if proline > 929.5 then class 1 (48 cases, 45 correct, 2 recovered by other rules). if color < 3.79285 then class 2 (63 cases, 60 correct)

23. IncNet Incremental Neural Network (IncNet). Ontogenic NN with single hidden layer, adding, removing and merging neurons. Transfer functions: Gaussians or combination of sigmoids (bi-central functions). Training: use Kalman filter approach to estimate network parameters. Fast Kalman filter training is usually sufficient. Always creates one network per class, separating it from other samples. Creates predictive models equivalent to fuzzy rules.

24. k-nearest neighbors Use various similarity functions to evaluate how similar new case is to all reference (training) cases, use p(Ci|X) = k(Ci)/k. Similarity functions include Minkovsky and similar functions. Optimize k, the number of neighbors included. Optimize the scaling factors of features Wi|Xi-Yi|: this goes beyond feature selection.Use search-based techniques to find good scaling parameters for features. Notice that: For k=1 always 100% on the training set is obtained! To evaluate accuracy on training use leave-one-out procedure.

25. Committees and K-classifiers K-classifiers: in K-class problems create K classifiers, one for each class. Committees: combine results from different classification models: create different models using the same method (for example decision tree) on different data samples (bootstraping); combine several different models, including other committees, into one model; use majority voting to decide on the predicted class. No rules, but stable and accurate classification models.

26. Summary Please get your copy from http://www.fqspl.com.pl/ghostminer/ Ghost Miner combines 4 basic tools for predictive data mining and understanding of data. GM includes K-classifiers and committees of models. GM includes MDS visualization/dimensionality reduction. Model building is separated from model use. GM provides tools for easy testing of statistical accuracy. Many new classification models are coming.