phd defense -- ashish mangalampalli

A Fuzzy Associative Rule-based Approach for Pattern

Mining and Pattern-based Classification

Ashish MangalampalliAdvisor: Dr. Vikram Pudi

Centre for Data Engineering International Institute of Information Technology (IIIT)

Hyderabad1

Outline Introduction

Crisp and Fuzzy Associative Classification

Pre-Processing and Mining Fuzzy Pre-Processing – FPrep Fuzzy ARM – FAR-Miner and FAR-HD

Associative Classification – Our Approach FACISME – Fuzzy Adaption of ACME (Maximum Entropy Associative Classifier) Simple and Effective Associative Classifier (SEAC) Fuzzy Simple and Effective Associative Classifier (FSEAC)

Associative Classification – Applications Efficient Fuzzy Associative Classifier for Object Classes in Images (I-FAC) Associative Classifier for Ad-targeting

Conclusions2

Introduction Associative classification

Mines huge amounts of data Integrates Association Rule Mining (ARM) with Classification

Associative classifiers have several advantages Frequent itemsets capture dominant relationships between

items/features Statistically significant associations make classification

framework robust Low-frequency patterns (noise) are eliminated during ARM Rules are very transparent and easily understood

Unlike black-box-like approach used in popular classifiers, such as SVMs and Artificial Neural Networks

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A = a, B = b, C = c → X = x




Associative Classification – Our Approach Simple and Effective Associative Classifier (SEAC) Fuzzy Simple and Effective Associative Classifier (FSEAC)


Conclusions

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Crisp Associative Classification Most associative classifiers are crisp

Most real-life datasets contain binary and numerical attributes Use sharp partitioning Transform numerical attributes to binary ones, e.g. Income = [100K

and above]

Drawbacks of sharp partitioning Introduces uncertainty, especially at partition boundaries Small changes in intervals lead to misleading results Gives rise to polysemy and synonymy Intervals do not generally have clear semantics associated

For example, sharp partitions for the attribute Income Up to 20K, 20K-100K, 100K and above Income = 50K would fit in the second partition But, so would Income = 99K

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Fuzzy Associative Classification Fuzzy logic

Used to convert numerical attributes to fuzzy attributes (e.g. Income = High)

Maintains integrity of information conveyed by numerical attributes

Attribute values belong to partitions with some membership - interval [0, 1]

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Conclusions7

Pre-Processing and Mining Fuzzy pre-processing

Convert crisp dataset (binary and numerical attributes) into fuzzy dataset (binary and fuzzy attributes)

FPrep Algorithm used

Efficient and robust Fuzzy ARM algorithms Web-scale datasets mandate such algorithms Fuzzy Apriori is most popular Many efficient crisp ARM algorithms exist like ARMOR

and FP-Growth Algorithms used

FAR-Miner for normal transactional datasets FAR-HD for high dimensional datasets

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Conclusions13

Associative Classification – Our Approach AC algorithms like CPAR and CMAR only mine frequent

itemsets Processed using additional (greedy) algorithms like FOIL and PRM Overhead in running time; process more complex

Association rules directly used for training and scoring Exhaustive approach

Controlled by appropriate support Not a time-intensive process

Rule pruning and ranking take care of huge volume and redundancy

Classifier built in a two-phased manner Global rule-mining and training Local rule-mining and training Provides better accuracy and representation/coverage14

Associative Classification – Our Approach (cont’d) Pre-processing to generate fuzzy dataset (for

fuzzy associative classifiers) using FPrep

Classification Association Rules (CARs) mining using FAR-Miner or FAR-HD

CARs pruning and classifier training using SEAC or FSEAC

Rule ranking and application (scoring) techniques

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Simple and Effective Associative Classifier (SEAC)

Direct mining of CARs – faster and simpler training

CARs used directly through effective pruning and sorting

Pruning and rule-ranking based on Information gain Rule-length

Two-phased manner Global rule-mining and training Local rule-mining and training

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SEAC - Example

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Ruleset

Example Dataset

Scoring ExampleUnlabeled: B=2, C=2X=1 → 16, 17, 19 (IG=0.534)X=2 → 13, 14, 20 (IG=0.657)

Fuzzy Simple and Effective Associative Classifier (FSEAC) Amalgamates Fuzzy Logic with Associative Classification

Pre-processed using FPreP

CARs mined using FAR-Miner / FAR-HD

CARs pruned based on Fuzzy Information Gain (FIG) and rule length - no sorting required

Scoring – rules applied taking µ into account Sorting done then Final score computed

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FSEAC - Example

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Example Dataset Fuzzy Version of Example Dataset

Format for Fuzzy Version of Dataset

FSEAC – Example (cont’d)

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Ruleset

SEAC and FSEAC Experimental Setup SEAC

12 classifiers (Associative and non-associative) 14 UCI ML datasets 100-5000 records per dataset 2-10 classes per dataset Up to 20 features per dataset 10-fold Cross Validation

FSEAC 17 classifiers (Associative and non-associative; fuzzy and crisp) 23 UCI ML datasets 100-5000 records per dataset 2-10 classes per dataset Up to 60 features per dataset 10-fold Cross Validation

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SEAC – Results (10 fold-CV)

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continued

SEAC - Results (10 fold-CV)

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FSEAC - Results (10 fold-CV)

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continued

FSEAC - Results (10 fold-CV)

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Conclusions

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Efficient Fuzzy Associative Classifier for Object Classes in Images (I-FAC) Adapts fuzzy associative classification for Object Class

Detection in images Speeded-Up Robust Features (SURF) - interest point detector

and descriptor for images Fuzzy clusters used as opposed to hard clustering used in

Bag-of-words

Only positive class (CP) examples used for mining Negative class (CN) in object class detection is very vague

CN = U – CP

Rules are pruned and ranked based on Information Gain Other AC algorithms use third-party algorithms for rule-

generation from frequent itemsets Top k rules are used for scoring and classification

ICPR 201027

I-FAC SURF points extracted from positive class images

FCM applied to derive clusters Clusters (with µs) used to generate dataset for mining

100 fuzzy clusters as opposed to1000-2000 crisp clusters-based algorithms

ARM generates Classification Association Rules (CARs) associated with positive class

CARs are pruned and sorted using Fuzzy Information Gain (FIG) of each rule Length of each rule i.e. number of attributes in each rule

Scoring based on rule-match and FIGICPR 201028

I-FAC - Performance Study Performs well when

compared to BOW or SVM Very well at low FPRs

(≤0.3)

Fuzzy nature helps avoid polysemy and synonymy

Uses only positive class for training

ICPR 201030

Visual Concept Detection on MIR Flickr Revamped version of I-FAC

Multi-class detection 38 visual concepts e.g. car, sky, clouds, water, building, sea, face

Experimental evaluation First 10K images of MIR Flick dataset AUC values for each concept

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Experimental Results (3-fold CV)

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continued

Experimental Results (3-fold CV)

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Look-alike Modeling using Feature-Pair-based Associative Classification Display-ad targeting currently done using methods which rely

on publisher-defined segments like Behavior-targeting (BT)

Look-alike model trained to identify similar users Similarity is based on historical user behavior Model iteratively rebuilt as more users are added Advertiser supplies seed list of users

Approach for building advertiser specific audience segments Complements publisher defined segments such as BT Provides advertisers control over the audience definition

Given a list of target users (e.g., people who clicked or converted on a particular category or ad campaign), find other similar users.

WWW 201134

Look-alike Modeling using Feature-Pair-based Associative Classification – cont’d Enumerate all feature-pairs in training set

occurring in at least 5 positive-class records Feature-pairs modelled as AC rules Only rules for positive class used Works well in Tail Campaigns

Affinity measured by Frequency-weighted LLR (F-LLR) FLLR = P(f) log(P(f | conv) / P(f | non-conv)) Rules sorted in descending order by F-LLRs

Scoring - Top k rules are applied Cumulative score from all rules used for classification

WWW 201135

Performance Study Two pilot campaigns

300K records each One record per user Training window - 14 days Scoring window - seven

days

Works very well for Tail Campaigns Can find meaningful

associations in extremely sparse and skewed data

SVM and GBDT work well for Head Campaigns

Baseline Lift

(Conversion Rate)

Lift (AUC)

Random Targeting 82% –

Linear SVM 301% 11%GBDT 100% 2%

Baseline Lift (Conversion Rate) Lift (AUC)

Random Targeting 48% –

Linear SVM -12% -6%GBDT -40% -14%

Results on a Tail Campaign

Results on a Head CampaignWWW 201136






Conclusions37

Conclusions Fuzzy pre-processing for dataset

transformation

Fuzzy ARM for various types of datasets

Fuzzy and Crisp Associative Classifiers for various domains Customizations required for different domains

Pre-processing Pruning Rule ranking techniques Rule application (scoring) techniques

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References Ashish Mangalampalli, Adwait Ratnaparkhi, Andrew O. Hatch, Abraham

Bagherjeiran, Rajesh Parekh, and Vikram Pudi. A Feature-Pair-based Associative Classification Approach to Look-alike Modeling for Conversion-Oriented User-Targeting in Tail Campaigns. In International World Wide Web Conference (WWW), 2011.

Ashish Mangalampalli, Vineet Chaoji, and Subhajit Sanyal. I-FAC: Efficient fuzzy associative classifier for object classes in images. In International Conference on Pattern Recognition (ICPR), 2010.

Ashish Mangalampalli and Vikram Pudi. FPrep: Fuzzy clustering driven efficient automated pre-processing for fuzzy association rule mining. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2010.

Ashish Mangalampalli and Vikram Pudi. FACISME: Fuzzy associative classification using iterative scaling and maximum entropy. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2010.

Ashish Mangalampalli and Vikram Pudi. Fuzzy Association Rule Mining Algorithm for Fast and Efficient Performance on Very Large Datasets. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2009.39

Thank You, andQuestions

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phd defense -- ashish mangalampalli

Technology

fuzzy associative classifiers

fac associative classifier

hd associative classification

fuzzy attributese

x associative classifiers

algorithms fuzzy apriori

classifier training

hdassociative classification