程序代做CS代考 information retrieval database data mining COMP3030 / COMP8430 Data wrangling – cscodehelp代写
COMP3030 / COMP8430 Data wrangling
Lecture 19: Record linkage evaluation (1) (Lecturer: )
Lecture outline
● Evaluating the record linkage process ● Linkage quality measures
● Linkage complexity measures
The record linkage process
Evaluating the record linkage process
● Different techniques are available for each step of the record linkage process (cleaning and standardisation, blocking, comparison, and
classification)
● When employing a record linkage system, one wants to get the
best possible results within operational constraints
(linkage time, computational resources, minimum linkage quality, available software and human expertise, etc.)
● Measures are required to evaluate the two main aspects of record
linkage
– Linkage quality (effectiveness) – Linkage complexity (efficiency)
Measuring linkage quality (1)
● Achieving high linkage quality is a main goal of most record linkage projects / applications
● Questions: What affects linkage quality?
What is required in order to be able to measure linkage quality?
Measuring linkage quality (2)
● Ground truth data is needed to measure linkage quality – A set of true matching record pairs
– A set of true non-matching record pairs
● How to obtain such ground truth data?
– Results of a previous linkage
– Manual clerical review (more in the next lecture) or manually classified
(sampled) record pairs
– Contact all individuals in the databases and ask them?
● How confident can one be such ground truth data is always correct?
Measuring linkage quality (3)
● Various difficulties with manually prepared ground truth data
– It is easy to classify record pairs that have totally different attribute values
as non-matches
– It is (generally) easy to classify record pairs that are very similar as matches
(but what about twins, or if not enough information is available?)
– It is difficult to classify record pairs where some attribute values are the
same/similar while others are different
– Studies have shown that manual classification of record pairs is never
100% correct
– Domain expertise is often required (such as knowledge about names and
their origins / cultures)
– If randomly sampled, most record pairs will be non-matches
Measuring linkage quality with ground truth (1)
● Assuming ground truth data are available, the classification of
record pairs into matches and non-matches has four possible
outcomes:
– True positives (TP): True matches correctly classified as matches
(correct matches)
– False negatives (FN): True matches incorrectly classified as non-matches
(false non-matches)
– True negatives (TN): True non-matches correctly classified as non-
matches (correct non-matches)
– False positives (FP): True non-matches incorrectly classified as matches
(false matches)
Measuring linkage quality with ground truth (2)
Measuring linkage quality with ground truth (3)
● Due to the quadratic comparison space, the number of true
non-matches is usually much larger than the number of true
matches
– As the number of records in the databases to be linked increases, the
number of true matches increases linearly while the number of possible record pairs increases quadratically
● This holds even after blocking
● Question: Assuming no duplicates in two databases with 1 and 5 million records, respectively, what is the maximum number of true matches between these two databases?
Error or confusion matrix (1)
Predicted classes
Matches
Non-matches
Actual classes
Matches
True positives (true matches)
False negatives (false non-matches)
Non-matches
False positives (false matches)
True negatives (true non-matches)
Error or confusion matrix (2)
● Based on the values in the four cells of the error/confusion matrix, different linkage quality measures can be defined
● These measures are binary classification measures as also used
in other domains
– Machine learning and data mining – Information retrieval (Web search) – Medical tests
– Security (airport screening), etc.
● There is often a trade-off between the number of false positives and false negatives (as one goes down the other goes up)
Accuracy
● Widely used in machine learning and data mining ● Considers both true positives and true negatives
acc = (TP + TN) / (TP + FP + FN + TN)
● Question: Is accuracy a good measure for record linkage? Why or why not?
Precision (or positive predictive value)
● Widely used in information retrieval (Web search) to assess the quality of search results (how many documents retrieved for a query are
relevant?)
● Considers only true positives
prec = TP / (TP + FP)
● For record linkage, it measures how many of the classified matches are true matches
Recall (or positive predictive value)
● Widely used in information retrieval to assess the quality of search results (how many of all relevant documents have been retrieved for a
query?)
● Considers only true positives
reca = TP / (TP + FN)
● For record linkage, it measures how many of all true matches have been classified as matches
F-measure: Combining precision and recall
● Precision and recall are often combined into the F-measure (or F-score):
fmeas = 2 * (prec * reca) / (prec + reca)
● It is the harmonic mean of precision and recall
● As precision goes up (e.g. lowering a similarity threshold),
recall goes down, and vice-versa
● But: Recent research has shown that comparing F-measure
results can be misleading (different weights given to precision and recall) (Hand and Christen, Statistics and Computing, 2018)
Visualising linkage quality results (1)
● Each of the presented measures is calculated based on a specific error / confusion matrix
● Each classifier, and each change in a classifier parameter, will
produce a different error matrix
– Lowering a classification threshold, t, will usually increase the numbers
of TP but also FP, and lower the numbers of TN and FN – Raising a classification threshold leads to the opposite
● To better understand classifiers and to compare them, plots are useful tools (for example for different classification thresholds)
Visualising linkage quality results (2)
● F-measure graph shows precision, recall and f-measure for different classifier thresholds
Visualising linkage quality results (3)
● Precision-recall graph shows precision versus recall for different classifier thresholds
Visualising linkage quality results (4)
● ROC (receiver operating curve) graph shows true positive rate versus false positive rate for different classifier thresholds
Measuring linkage complexity
● We can easily measure run-time and memory consumption of a linkage program / system (but is this meaningful)?
● Generally, platform independent measure are better
– Allows the performance of systems to be compared even when not run
on the same computing platform (but same data sets and same parameter settings)
● Linkage complexity is generally measured by the number of
record pairs that need to be compared
– The number of candidate record pairs generated by blocking
Reduction ratio
● Measures by how much a blocking technique is able to reduce the comparison space
– Compared to the full pair-wise comparison of all record pairs
rr=1–(sM +sN)/(nM +nN)
where:
– sM and sN are the number of true matching and non-matching candidate
record pairs generated by a blocking technique
– nM and nN are the total number of true matching and non-matching record
pairs (in the pair-wise comparison space)
Pairs completeness
● Measures how many true matches ‘pass’ through a blocking process
● It corresponds to the recall of blocking pc=sM /nM
● It requires the truth match status of all record pairs (as with the linkage quality measures)
Pairs quality
● Measures how many candidate record pairs generated by blocking are true matches
● It corresponds to the precision of blocking pq=sM /(sM+sN)
● It requires the truth match status of all record pairs (as with the linkage quality measures)
