Scaling up classification rule induction through parallel - - PDF document
11/21/2018 Scaling up classification rule induction through parallel processing Presented by Melissa Kremer and Pierre Duez Background/Motivation Large datasets require parallel approaches Datasets can be: Extremely large (NASAs
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Presented by Melissa Kremer and Pierre Duez
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○ Geographically distributed data mining ■ Multiple data sources ■ Collaboration between multiple stakeholders ■ Cost/logistics to collating all data in one location ○ Computationally distributed data mining ■ Also referred to as “parallel data mining” ■ Scaling of data mining by distributing the load to multiple computers ■ Borne of a single, coherent dataset
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○ By number of attributes: ■ Keep n top attributes ■ Keep x% of the attributes with highest gain ○ By information gain ■ Keep all attributes whose information again is at least x% of the best attribute ■ Keep all attributes whose information gain is at least x%
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How to choose the best attributes? From Han and Kamber (2001):
… just do PCA.
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○ SRSWOR (Simple Random Sample WithOut Replacement) ○ SRSWR (Simple Random Sample With Replacement)
○ How to choose sample size? ○ OR How to tell when your sample is sufficiently representative?
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○ Start with user‐specified window size ○ Use sample (“window”) to train initial classifier ○ Apply classifier to remaining samples in dataset (until limit of misclassified examples is reached) ○ Add misclassified samples to the window and repeat
○ Does not do well on noisy datasets ○ Multiple classification/training runs ○ Naive stop conditions
Extensions to Windowing (Quinlan, 1993) including:
skewed datasets
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○ “Consistent Rule”: rule that did not misclassify a negative example
model accuracy plateaus
about accuracy vs training set size
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○ The dataset may be divided into equal sizes, or sizes may reflect speed
○ a learning algorithm is used to learn rules from local dataset ○ Algorithms may or may not communicate training data or information about the training data
○ Use a combining procedure to form a final concept description
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○ every rule that is acceptable globally
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Conquer” approach:
○ Select attribute (A) to split on ○ Divide instances in the training set into subsets for each value of A ○ Recurse along each leaf until stop condition (pure set, exhausted attributes, no more information gain, etc.)
○ Repeatedly generate rules to cover as much of the outstanding training set as possible ○ Remove samples covered by the rules from the set, and train new rules on the remaining examples
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processor has its own memory, bus
distribution characteristics and collectively determine next partition
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Advantages
Disadvantages
in higher nodes; cannot be rebalanced if one processor finishes early
○ Begin with synchronous tree construction ○ When communication cost becomes too high, partition tree for later stages
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class index> pairs (as well as a <class‐label, node> list for the classes.
○
Sorting for each attribute only has to happen once
projected table (and the class/node table) at a time
the <class‐label, node> table.
○ SLIQ‐R (replicate class list) ○ SLIQ‐D (distributed class list) ○ Scalable Parallelizable Induction of Decision Trees (SPRINT)
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SLIQ : SPRINT:
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Advantages:
time Disadvantages:
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classifiers on new data
○ Voting (majority or weighted) ○ Arbitration (if consensus is not reached) ○ Combining (rule set for classifiers)
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Images taken from: https://www.youtube.com/watch?v=N_V2BmjeYLw
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E.g. instances have the same values for attributes but are assigned to different classes
○
These can be dealt with by discarding the rule if the target class is not the majority class, then deleting the instances that are assigned to the discarded rule’s target class
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to induce locally best rule terms.
plus its covering probability on the local Rule Term Partition
Term Partition and writes the name of the KS that induced the best rule term on the "Global Information Partition"
○ IF KS is winning expert { ■ keep locally induced rule term and derive by last induced rule term uncovered ids and write them on the Global Information ■ Partition and delete uncovered list records } ○ ELSE IF KS is not winning KS { ■ delete the locally induced rule term and wait for by best rule term uncovered ids being available on the Global Information Partition, download them ■ delete list records matching the retrieved ids. }
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list
Prism algorithm
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Strengths:
size of the datasets
Weaknesses:
must also be parallelised. Explored in other papers
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that cannot be held in memory
processors & more memory, but slower communication between processors)
construction, ensemble learning
PMCRI
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multiprocessor systems: “Nowadays, dual‐core processors are standard technology, but in the near future we will have multi‐core processors in our personal computers.” (emphasis in the original)
that many of these lessons will continue to apply.