计算机代考程序代写 Bayesian decision tree algorithm COMP3430 / COMP8430 Data wrangling – cscodehelp代写

COMP3430 / COMP8430 Data wrangling
Lecture 18: Record pair classification (2) (Lecturer: )

Lecture outline
● Cost based classification
● Rule based classification
● Machine learning based classification ● Managing transitive closure

Cost based classification (1)
● In record linkage classification we can make two types of mistakes (1) A record pair that is a true match (same entity) is classified as a
non-match (false negative)
(2) A record pair that is a true non-match (different entities) is classified as a
match (false positive)
● Traditionally it is assumed both types of errors have the same
costs
● Question: In which applications / situations do these two types
of errors have different costs?

Cost based classification (2)
● If costs for mis-classification are known (or can be estimated), a cost-optimal decision can be made
● Based on the probabilistic record linkage approach (previous lecture), for record pair r we can calculate the overall cost c as:
c(r)=cU,U *P(r∈non-match,r∈U)+cU,M *P(r∈non-match,r∈M)+ cM,U *P(r∈match,r∈U)+cM,M *P(r∈match,r∈M)
where the record pair is classified as a match or non-match while
its true match status is M or U
● The aim is to minimise the overall costs c for all record pairs

Rule based classification (1)
● A different approach compared to probabilistic record linkage
● A set of rules is used to classify a record pair as a match or
non-match (and possibly a potential match)
● Rules are applied on the calculated attribute similarities, where
individual tests are combined using logical operations (AND, OR, NOT)
● The ordering of rules is important if different rules in a rule set
classify a record pair into matches and non-matches (i.e. which
rules are applied first)
(several rules might trigger (be true) for a given record pair)

Rule based classification (2)
● Example rules:
(sim(GivenName)[ri, rj] ≥ 0.9) ∧ (sim(Surname)[ri, rj] = 1.0) ∧
(sim(BMonth)[ri, rj] = 1.0) ∧ (sim(BYear)[ri, rj] = 1.0): [ri, rj] → Match
(sim(GivenName)[ri, rj] ≥ 0.7) ∧ (sim(Surname)[ri, rj] ≥ 0.8) ∧ (sim(StrName)[ri, rj] ≤ 0.6) ∧ (sim(Suburb)[ri, rj] ≤ 0.6):
[ri, rj] → Non-Match

Rule based classification (3)
● Rules should have high accuracy and high coverage
– High accuracy means they correctly classify record pairs that are
covered by the rule into their correct class (of matches and
non-matches)
– High coverage means a rule covers a large number of all record pairs
(not just a few)
● Rule sets can be build manually or they can be learned
– Manually based on domain knowledge (time-consuming and expensive) – Learning based requires training data in the form of true matching and
non-matching record pairs

Machine learning based classification (1)
● Machine learning algorithms learn patterns, classes, rules, or clusters from data
● Supervised techniques require training data in the form of ground
truth (for record linkage: record pairs of true matches and true non-matches) – These are classification and regression techniques
– Example techniques are decision trees, support vector machines,
neural networks, logistic regression, Bayesian classifiers, etc.
● Unsupervised techniques do not require training data
– They cluster similar data points, or extract frequent patterns and rules
from data
– Example techniques are clustering and association rule mining

Machine learning based classification (2)
● Many machine learning techniques have been used / adapted for record linkage
● A major challenge for supervised techniques is to obtain training
data of good quality and variety
– Actual truth often not known (for example it is impossible to call all
individuals that correspond to true matches)
– Easy to get clear true matches and non-matches
– Difficult to get borderline cases (such as same or similar name and
different address)
● Another challenge is the class imbalance
(many more non-matching record pairs compared to matching ones)

Machine learning based classification (3)
● Example: Decision trees learned using a small training data set
GName
SName
StNum
StName
Suburb
BDay
BMonth
BYear
Class
0.6
0.8
0.0
1.0
0.6
0.5
0.5
1.0
M
0.0
0.15
0.0
0.5
0.0
0.5
0.0
0.75
U
0.2
0.0
0.0
0.1
0.15
0.0
0.0
0.75
U
0.0
0.25
1.0
0.4
0.6
1.0
1.0
0.75
M

Managing transitive closure
● When record pairs are classified individually, the result might be inconsistent with regard to transitivity
● If record a1 is classified as a match with record a2, and
a2 is classified as a match with record a3, then a1 and a3 must also be a match
● Special post-processing and
clustering techniques need to be applied