Taxonomy Lecture 12

Topics Tutorial Review Classification Frame Terminology Classical Taxonomy Using Classifications –In system use –In system development Review Preview

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)

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

Example Calculation : filtering Good Spam 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 TP FP FN TN 46

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), 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 “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

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

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

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

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

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

ABC Classifier Machine Human Categories/Classes Taxonomy

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

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

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

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

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

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?

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

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

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 – 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?

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

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

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