Taxonomy

1. Taxonomy Lecture 12

2. Topics • Tutorial Review • Classification Frame • Terminology • Classical Taxonomy • Using Classifications • In system use • In system development • Review • Preview

3. Tutorial Review-Dating System • Outer join • to include unmatched persons as well as matched • Select .. from person left join pair • to include only unmatched : • where partner is null • (from Placement visit) use with reference tables • Do updates before displaying status • Use tables within tables for layout • Complex calculation has to be repeated • In Oracle/ SQL server , procedure can be stored in DBMS • Multi-user issues • Use single queries or transactions for atomicity • Still get problems with ‘dirty’ data – screen allows match for ‘albert’ but albert already matched • Refresh shows blank screen – now refresh home screen only • An Update would be rejected (but can be ignored)

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

5. Example Calculation : email filtering Good Email Spam 7 11 • 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 accept 3 5 FP TP 4 6 reject TN FN

6. Classification and Systems Design “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 • Steps in Classification • defining the domain (what kinds of things are to be classified) • creating the taxonomy (the set of categories), is 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

7. 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

8. Categories in Information Systems • Many systems require the user to classify things in the real world into categories in order to process them: • Files and documents on disk • Facilities in the University (helpdesk, reception.. • Skills in a Placements system • Budget headings, Nominal Ledger headings • Complaints • Fault priority • On the system, categories can be clearly distinguished: • Codes for each category • But the user typically has the task of mapping the real, complex things into the appropriate categories and interpreting categorical information

9. 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

10. Terminology • Category/ Class • A group of similar objects • Binary Category • An object is either in the category or not • Taxonomy • A set of Categories, sometimes organised into a hierarchy, for a common purpose • Multiple Taxonomies may be applied to the same population of objects • Categorisation/ Classification • The task of placing objects into the appropriate Category / Class • Clustering • The process of identifying similar objects

11. A dodgy taxonomy • The Argentinean writer Jorge Luis Borges ‘Imaginary Beasts’, ‘Labyrinths’..) quotes a ‘certain Chinese encyclopedia’ 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

12. Machine Classifier Human Categories/Classes A B C Taxonomy

13. Categories not Mutually Exclusive Machine An object can be put in any of several categories Classifier Human Categories/Classes A B C Taxonomy

14. Categories not Complete Machine Classifier Some objects don’t belong anywhere Human Categories/Classes A B C Taxonomy

15. Categories not Balanced Machine Some categories much larger than others Classifier Human Categories/Classes A B C Taxonomy

16. Categories Inconsistant Machine Categories lack a single organising principle Classifier Human Categories/Classes A B C Taxonomy

17. Taxonomy design • 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 • Consistant • Same characteristics used throughout

18. Kinds of classification • Classical • Classes defined by presence of features • Square : 4 sides, equal length, equal angles • Rectangle : 4 sides, 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?

19. 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

20. 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

21. Tutorial • Read ‘Ten Taxonomy Myths’ • Problem: • A team of consultants has been hired to assist a local voluntary organisation whose aim is to help local people locate organisations which provide relevant services. • They have a web site and publish a newsletter • www.eastbristoladvice.org.uk • How would you advise them to classify the organisations for ease of recall? What taxonomies would appropriate? Binary or multi-category? • What information would you hold about each organisation? • How would you gather information on the effectiveness of your taxonomies?

22. Review • 3 tier, 4-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 • DBMS comparison and selection • Entity-Relationship modelling – revision, application • Data flow - specification of data flows, XML • Sequence diagrams – construct from description • 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

23. Preview • Learning Frame • Business Processes • Scenarios and Use cases • Object-Relational DBMS • Data Quality • ….

24. Black board • Suggest additional topics • Suggest additional resources • Ask questions • Give me feedback