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ML410C Projects in health informatics – Project and information management Data Mining

ML410C Projects in health informatics – Project and information management Data Mining

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ML410C Projects in health informatics – Project and information management Data Mining

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  1. ML410CProjects in health informatics – Project and information managementData Mining

  2. Last time… • Why do we need data analysis? • What is data mining? • Examples where data mining has been useful • Data mining and other areas of computer science and mathematics • Some (basic) data mining tasks

  3. The Knowledge Discovery Process Knowledge Discovery in Databases (KDD) is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From Data Mining to Knowledge Discovery in Databases”, AI Magazine 17(3): 37-54 (1996)

  4. CRISP-DM: CRossIndustry Standard Process for Data Mining Shearer C., “The CRISP-DM model: the new blueprint for data mining”, Journal of Data Warehousing 5 (2000) 13-22 (see also

  5. Today

  6. Today • What is classification • Overview of classification methods • Decision trees • Forests

  7. Predictive data mining • Our task • Input: data representing objects that have been assigned labels • Goal: accurately predict labels for new (previously unseen) objects

  8. Features (attributes) An example: email classification Examples (observations)

  9. Decision tree Spam = yes Spam = yes Spam = no

  10. Rules Spam = no Spam = yes Spam = yes Spam = no Spam = no

  11. Forests

  12. Classification • What is the class of the following e-mail? • No Caps: Yes • No. excl. marks: 0 • Missing date: Yes • No. digits in From: 4 • Image fraction: 0.3

  13. Classification • What is classification? • Issues regarding classification and prediction • Classification by decision tree induction • Classification by Naïve Bayes classifier • Classification by Nearest Neighbor • Classification by Bayesian Belief networks

  14. Classification • Classification: • predicts categorical class labels • classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute • uses the model for classifying new data • Typical Applications • credit approval • target marketing • medical diagnosis • treatment effectiveness analysis

  15. Why Classification? A motivating application • Credit approval • A bank wants to classify its customers based on whether they are expected to pay back their approved loans • The history of past customers is used to train the classifier • The classifier provides rules, which identify potentially reliable future customers

  16. Why Classification? A motivating application • Credit approval • Classification rule: • If age = “31...40” and income = high • then credit_rating= excellent • Future customers • Paul: age = 35, income = high  excellent credit rating • John: age = 20, income = medium  fair credit rating

  17. Classification—A Two-Step Process • Model construction: describing a set of predetermined classes • Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute • The set of tuples used for model construction: training set • The model is represented as classification rules, decision trees, or mathematical formulas

  18. Classification—A Two-Step Process • Model usage: for classifying future or unknown objects • Estimate accuracy of the model • The known label of test samples is compared with the classified result from the model • Accuracy rate is the percentage of test set samples that are correctly classified by the model • Test set is independent of training set, otherwise over-fitting will occur

  19. Training Data Classifier (Model) Classification Process (1): Model Construction Classification Algorithms IFLDL = ‘high’ ORGluc > 6 mmol/lit THENHeart attack = ‘yes’

  20. Classifier Testing Data Unseen Data Classification Process (2): Use the Model in Prediction Accuracy=? (Jeff, high, 7.5) Heart attack?

  21. Supervised vs. Unsupervised Learning • Supervised learning (classification) • Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations • New data is classified based on the training set • Unsupervised learning(clustering) • The class labels of training data is unknown • Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data

  22. Issues regarding classification and prediction: Evaluating Classification Methods • Predictive accuracy • Speed • time to construct the model • time to use the model • Robustness • handling noise and missing values • Scalability • efficiency in disk-resident databases • Interpretability: • understanding and insight provided by the model • Goodness of rules (quality) • decision tree size • compactness of classification rules

  23. Classification by Decision Tree Induction • Decision tree • A flow-chart-like tree structure • Internal node denotes a test on an attribute • Branch represents an outcome of the test • Leaf nodes represent class labels or class distribution • Decision tree generation consists of two phases • Tree construction • At start, all the training examples are at the root • Partition examples recursively based on selected attributes • Tree pruning • Identify and remove branches that reflect noise or outliers • Use of decision tree: Classifying an unknown sample • Test the attribute values of the sample against the decision tree

  24. Training Dataset Example

  25. Output: A Decision Tree for “buys_computer” age? overcast <=30 >40 30..40 student? credit rating? yes no yes fair excellent no yes no yes

  26. Algorithm for Decision Tree Induction • Basic algorithm (a greedy algorithm) • Tree is constructed in a top-down recursive divide-and-conquer manner • At start, all the training examples are at the root • Attributes are categorical (if continuous-valued, they are discretized in advance) • Samples are partitioned recursively based on selected attributes • Test (split) attributes are selected on the basis of a heuristic or statistical measure (e.g., information gain) • Conditions for stopping partitioning • All samples for a given node belong to the same class • There are no remaining attributes for further partitioning – majority voting is employed for classifying the leaf • There are no samples left

  27. Algorithm for Decision Tree Induction (pseudocode) Algorithm GenDecTree(Sample S, Attlist A) • create a node N • If all samples are of the same class C then label N with C; terminate; • If A is empty then label N with the most common class C in S (majority voting); terminate; • Select aA, with the highest information gain; Label N with a; • For each value v of a: • Grow a branch from N with condition a=v; • Let Sv be the subset of samples in S with a=v; • If Sv is empty then attach a leaf labeled with the most common class in S; • Else attach the node generated by GenDecTree(Sv, A-a)

  28. Attribute Selection Measure: Information Gain • Let pi be the probability that an arbitrary tuple in D belongs to class Ci, estimated by |Ci, D|/|D| • - where Ci, Ddenotes the set of tuples that belong to class Ci • Expected information (entropy) needed to classify a tuple in D: • - where m is the number of classes

  29. Attribute Selection Measure: Information Gain • Informationneeded (after using A to split D into v partitions) to classify D: • Information gained by branching on attribute A

  30. Class P: buys_computer = “yes” Class N: buys_computer = “no” Attribute Selection: Information Gain

  31. Splitting the samples using age age? >40 <=30 30...40 labeled yes

  32. Giniindex • If a data set D contains examples from n classes, gini index, gini(D) is defined as - where pj is the relative frequency of class j in D • If a data set D is split on A into two subsets D1 and D2, the gini index gini(D) is defined as

  33. Giniindex • Reduction in Impurity: • The attribute that provides the smallest ginisplit(D) (or the largest reduction in impurity) is chosen to split the node

  34. Gini index (CART, IBM IntelligentMiner) Example: • D has 9 tuples in buys_computer = “yes” and 5 in “no” • Suppose that attribute “income” partitions D into 10 records (D1: {low, medium}) and 4 records (D2: {high}).

  35. Giniindex • Then: = 0.45 and gini{medium,high} = 0.30 • All attributes are assumed continuous-valued • May need other tools, e.g., clustering, to get the possible split values

  36. Comparing Attribute Selection Measures • The two measures, in general, return good results but • Information gain: • biased towards multivalued attributes • Giniindex: • biased to multivalued attributes • has difficulty when # of classes is large • tends to favor test sets that result in equal-sized partitions and purity in both partitions

  37. Overfitting due to noise Decision boundary is distorted by noise point

  38. Overfitting due to insufficient samples Why?

  39. Overfitting due to insufficient samples Lack of data points in the lower half of the diagram makes it difficult to predict correctly the class labels of that region - Insufficient number of training records in the region causes the decision tree to predict the test examples using other training records that are irrelevant to the classification task

  40. Overfitting and Tree Pruning • Overfitting: An induced tree may overfit the training data • Too many branches, some may reflect anomalies due to noise or outliers • Poor accuracy for unseen samples • Two approaches to avoid overfitting • Prepruning: Halt tree construction early—do not split a node if this would result in the goodness measure falling below a threshold • Difficult to choose an appropriate threshold • Postpruning: Remove branches from a “fully grown” tree—get a sequence of progressively pruned trees

  41. Occam’s Razor • Given two models of similar generalization errors, one should prefer the simpler model over the more complex model • Therefore, one should include model complexity when evaluating a model “entia non suntmultiplicandapraeterecessitatem”, which translates to: “entities should not be multiplied beyond necessity”.

  42. Pros and Cons of decision trees • Cons • Cannot handle complicated relationship between features • simple decision boundaries • problems with lots of missing data • Pros • Reasonable training time • Fast application • Easy to interpret • Easy to implement • Can handle large number of features

  43. Some well-known decisiontree learning implementations CARTBreiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees. Wadsworth ID3Quinlan JR (1986) Induction of decision trees. Machine Learning 1:81–106 C4.5 Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann J48 Implementation of C4.5 in WEKA

  44. Remove attributes with missing values • Remove examples with missing values • Assume most frequent value • Assume most frequent value given a class • Learn the distribution of a given attribute • Find correlation between attributes Handling missing values

  45. Handling missing values A1 no yes e1 (w=1) e3 (w=1) e4 (w=2/3) e2 (w=1) e4 (w=1/3)

  46. k-nearest neighbor classifiers k-nearest neighbors of a record x are data points that have the k smallest distance to x

  47. k-nearest neighbor classification • Given a data record x find its k closest points • Closeness: ? • Determine the class of x based on the classes in the neighbor list • Majority vote • Weigh the vote according to distance • e.g., weight factor, w = 1/d2

  48. Characteristics of nearest-neighbor classifiers • No model building (lazy learners) • Lazy learners: computational time in classification • Eager learners: computational time in model building • Decision trees try to find global models, k-NN take into account local information • K-NN classifiers depend a lot on the choice of proximity measure

  49. Condorcet’s jury theorem If each member of a jury is more likely to be right than wrong, then the majority of the jury, too, is more likely to be right than wrong and the probability that the right outcome is supported by a majority of the jury is a (swiftly) increasing function of the size of the jury, converging to 1 as the size of the jury tends to infinity Condorcet, 1785

  50. Condorcet’s jury theorem