1. Classification Lecture 11

2. Topics Tutorial Review Classification Frame Terminology and measures Using Classifications –In system use –In system development Creating Classifications –Card sorting

3. Fuzzy Matching in the Telephone Directory UWE telephone directory –Only fuzzy matching is partial matching on initial string ‘wall’ finds ‘wallace’, ‘wallis’, ‘walls’, … –Easy to do in SQL –..where surname like ‘reqsurname%’ –Substring matching anywhere is slower –.. Where surname like ‘%reqsurname%’

4. Telephone Schema Facilities(‘help desk’, ‘reception’ etc) forced to fit Person schema Lack of inclusion in schema creates searching problems: –Helpdesk –CSM help desk No support for categories of facility to control vocabulary –A Naming and Classification problem Need for generalisation: Person Surname : str Firstname : str ExtNo : str Contact Person Facility

5. Dating Problem

6. Distance (fitness) function Distance (P1, P2) = –Distance(P1, P2-Pref) + Distance(P2,P1-Pref) Individual differences: –agediff = if P1.age P2-Pref.max ? 1000 : 1 – abs(P1.age / ((P2-Pref.min+P2-Pref.max)/2 )) –gendiff = P1.gen == P2-Pref.gen ? 1000 : 0 –s1diff = abs(P1.s1 – P2-Pref.s1) –s2diff = abs(P1.s2 – P2-Pref.s2) Combined weighted differences –Euclidean distance –sqrt (wtage*agediff^2 + wtgen*gendiff^2 + wts1*s1diff^2 + wts2*s2diff^2…..) Problems –Age is a ratio scale (40 is twice as old as 20) –Preference scales are not – rating a scenario a 6 does not imply it is twice as good as a rating of 3 – Preference scales are Ordinal –Age and Gen are go-no go – simulated by very high value for a mismatch

7. Classification Frame Classification separates candidates into two or more classes –classifying students by grade of degree We will look at the simple case of two classes first: –filtering Email : Good or Spam –retrieving documents : Relevant or Irrelevant –classifying credit card transactions : Valid or fraudulent –detecting spelling mistakes : ok or mistake (red line) –medical testing : normal or abnormal –Systems Requirement : ambiguous or not abmiguous METAPHOR : SYSTEM IS A SIEVE

8. Classification Errors (Information Retrieval) RelevantIrrelevant Retrieved Not retrieved true negative true positive false negative (Type II error) false positive (Type 1 error) Precision = TP/ (TP + FP) = TP/ Retrieved Recall = TP / (TP + FN) = TP / Relevant Efficiency = (TP + TN) / (TP + TN + FP + FN) = (TP+TN) / Full Collection

9. Example Calculation : email filtering Good EmailSpam reject accept Precision = TP/ (TP + FP) = Recall = TP / (TP + FN) = Efficiency = (TP + TN) / (TP+TN+FP+FN) = 711 3 5

10. Example Calculation : email filtering Good EmailSpam reject accept Precision = TP/ (TP + FP) = 3/8 Recall = TP / (TP + FN) = 3/7 Efficiency = (TP + TN) / (TP+TN+FP+FN) = 9/18= 50% Recall > Precision => not quite balanced 711 3 5 TP FP FN TN 46

11. Trade-off The two errors are usually in conflict –we can decrease the risk of a False Positive (reject more Spam) –but –we increase the risk of False Negatives (rejecting good email) a TRADE-OFF

12. Classification Errors Good studentPoor student Pass Fail Write in the terms – relevant, retrieved, true positive, false positive etc

13. Improved Precision Precision = TP/ (TP + FP) = TP/ Retrieved Recall = TP / (TP + FN) = TP / Relevant TP -True Positives relevant TN - True Negatives FN - False Negatives retrieved FP - False Positives

14. Precision and Recall Precision = TP/ (TP + FP) = TP/ Retrieved Recall = TP / (TP + FN) = TP / Relevant Efficiency = (TP + TN) / (TP + TN + FP + FN) = (TP+TN) / Full Collection TP -True Positives relevant TN - True Negatives FN - False Negatives retrieved FP - False Positives Full collection

15. Improved Recall Precision = TP/ (TP + FP) = TP/ Retrieved Recall = TP / (TP + FN) = TP / Relevant TP -True Positives relevant TN - True Negatives FN - False Negatives retrieved FP - False Positives

16. Exercise: Precision and Recall in Assessment Precision means …… Recall means …. Ideal values (as %) –Precision= –Recall= –Efficiency Estimated values –Precision= –Recall= –Efficiency

17. Classification in the News Criminal Justice as a Classifer –Murder, Manslaughter or Innocent Is ‘Munchausen by Proxy’ a real psychological condition? Prisoners of war – US invents a new category for the Quantanamo Bay prisoners Blood groups: –A,B,AB,O –RH+, RH- Classification of Cloud types (Cumulus, Cirrus…) by Luke Howard 1802 Hip evaluation to determine priority for replacement Text classification to bring sense to the Internet

18. Categories are Information Structures Many systems require the user to classify things in the real world into categories in order to process them: –Files and documents on disk –Chapters in a dissertation –Facilities in the University (helpdesk, reception.. –Skills in a Placements system –Budget headings, Nominal Ledger headings In the computer system, categories can be clearly distinguished: –Codes for each category In the real world: –categories don’t exist The fallacy of misplaced concreteness –multiple taxonomies are valid – classifying the same things in different ways for different purposes Users typically has the task of –mapping the real, complex things into the appropriate categories –interpreting categorical information Implications –IS designers have to devise support for these tasks as well. –Users will not be consistent in their classification (e.g. IS books in Library)

19. Categories in IS theory Much of IS theory is based on a taxonomy: –Problem /solution –Method/methodology/technique.. –ER model –Data Flow Diagram –Soft Systems Analysis - CATWOE –Logical /Physical –Swot analysis Strengths/Weaknesses/Opportunities/Treats –Objective, Goal, Requirement, Constraint

20. Classification and Systems Design Steps in Classification –defining the domain (what kinds of things are to be classified) –creating the taxonomy (the set of categories), its purpose and force –defining the representation of individuals –defining the mapping between individuals and categories –coding the categories –creating automatic classifiers –assisting human classifiers –assisting users to interpret categorical information –evaluating classification performance –supporting evolution of taxonomy and classifiers “An early step towards understanding any set of Phenomena is to learn what kinds of things there are in the set – to develop a taxonomy” Herbert Simon

21. A Poor Classification? The Argentinean writer Jorge Luis Borges ‘Imaginary Beasts’, ‘Labyrinths’..) quotes a ‘certain Chinese encyclopaedia’ in which animals are divided into: A) belonging to the Emperor B) embalmed C) tame D) suckling pigs E) sirens F) fabulous G) stray dogs H) included in the present classification I) frenzied J) innumerable K) drawn with a very fine camel hair brush L) et cetera M) having just broken the water pitcher N) that from a long way off look like flies

22. ABC Classifier Machine Human Categories/Classes Taxonomy

23. ABC Classifier Machine Human Categories/Classes Taxonomy Categories not Mutually Exclusive An object can be put in any of several categories

24. ABC Classifier Machine Human Categories/Classes Taxonomy Categories not Complete Some objects don’t belong anywhere

25. ABC Classifier Machine Human Categories/Classes Taxonomy Categories not Balanced Some categories much larger than others

26. ABC Classifier Machine Human Categories/Classes Taxonomy Categories Inconsistant Categories lack a single organising principle

27. Characteristics of a good Taxonomy Categories must be: –Mutually exclusive Every object in at most one category –Complete (exhaustive) Every object in at least one category –Balanced Categories divide objects evenly –Consistent Same characteristics used throughout –Hierarchical integrity Categories at one level not confused with categories at another level

28. Kinds of classification Classical –Classes defined by presence of features Square : 4 sides, equal length, equal angles Triangle : 3 sides, equal length, equal angles Probabilistic –Classes defined by weighted sum of features ‘bird’ moves, winged, feathered, sings, lays eggs Is a robin a bird? Is a emu a bird? Exemplar (prototype) –Classes defined by one or more key examples Robin is a central example of ‘bird’ Chicken is more remote example Which kind is used in IS Theory? Which kind is used in IS Use?

29. Automated Clustering Clustering techniques find groups of similar objects Used in data mining to identify customer groups with similar buying behaviour… Mathematical Techniques –k-nearest neighbour –ID3 to create decision tree Human Techniques –Card sorting

30. Classifying Learning Classifiers –Based on sample of population –Classified by hand –Split into two parts The training set used to compute the classifier The test set used to test the ability of the classifier –Many kinds of classifiers available, all need good understanding of statistics e.g. Naïve Bayesian, Decision Tree, SVM –Threshold set to balance recall and precision Rule and example based for human classifier but performance varies with experience and skill –E.g. book classification, Yahoo directory classification, medical diagnosis –Human classifiers need to be trained too –If classification done by end-users, classification is likely to be inconsistent

31. Review 3 tier web architecture – describe, explain, terminology, typical interactions SQL & PHP –No exam questions to write SQL or PHP but reading knowledge required – up to outer joins and example scripts Extended ER models Interaction in human and computer systems – sequence diagrams SMS and its applications Web services Agile Development and Extreme Programming – description, application, comparison with life-cycle Frames – rationale, role in IS development, basic recognition in a problem description of simple frames and the following in detail Matching Frame – typical applications, fitness function, recognising nominal, ordinal, interval and ratio scales, use of weights Classification Frame – typical applications, terminology, calculation of recall and precision, guidelines for constructing a taxonomy

32. Preview XML and XSLT Business Processes Scenarios and Use cases Data Quality Learning Frame