Efficient Multi-label Classification
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1 Efficient Multi-label Classification Jesse Read (Supervisors: Bernhard Pfahringer, Geoff Holmes) November 2009
2 Outline 1 Introduction 2 Pruned Sets (PS) 3 Classifier Chains (CC) 4 Related Work 5 Experiments 6 Scaling up to Large Datasets 7 Summary
3 Multi-label Classification Single-label (Multi-class) Classification Examples: D = {x 1,, x n } Labels: L = {l 1,, l m } Each example is associated with one label: (x, l L) Multi-label Classification Examples: D = {x 1,, x n } Labels: L = {l 1,, l m } Each example is associated with a subset of labels: (x, S L)
4 Multi-label Classification Single-label (Multi-class) Classification Examples: D = {x 1,, x n } Labels: L = {l 1,, l m } Each example is associated with one label: (x, l L) Multi-label Classification Examples: D = {x 1,, x n } Labels: L = {l 1,, l m } Each example is associated with a subset of labels: (x, S L)
5 Multi-label Data - Dataset Statistics D L avg. S uniq.s Type Music media Scene media Yeast biology Genbase biology Medical medical text Slashdot news Lang.Log forum Enron Reuters(avg) news OHSUMED medical text tmc text Media Mill media Bibtex text IMDB text del.icio.us text
6 Multi-label Data - Label Correlations romance horror adult drama comedy western Freq. Combination (S L) / L = drama short documentary adult comedy documentary,short comedy,short drama,short animation,short 6550 action crime,drama 4042 horror documentary,war 342 comedy,western 361 action,sci-fi horror,romance 21 adult,western family Figure: Label correlations: IMDB subset.
7 Multi-label Data - Label Correlations horror romance adult drama family comedy western Figure: Label correlations: IMDB subset. Freq. Combination (S L) / L = drama short documentary adult comedy documentary,short comedy,short drama,short animation,short 6550 action crime,drama 4042 horror documentary,war 342 comedy,western 361 action,sci-fi horror,romance 21 adult,western where {horror,romance} S Thriller,{horror, romance, short, thriller, music} Love at First Bite,{horror, comedy, romance} Kondom des Grauens,{comedy, horror, romance}...
8 Multi-label Data - Label Correlations horror romance adult drama family comedy western Figure: Label correlations: IMDB subset. Freq. Combination (S L) / L = drama short documentary adult comedy documentary,short comedy,short drama,short animation,short 6550 action crime,drama 4042 horror documentary,war 342 comedy,western 361 action,sci-fi horror,romance 21 adult,western where {horror,romance} S Thriller,{horror, romance, short, thriller, music} Love at First Bite,{horror, comedy, romance} Kondom des Grauens,{comedy, horror, romance}... where {adult,western} S Hard on the Trail, {action, adult, western} Good the Bad the Nasty, The, {adult, comedy, western} Ride a Wild Stud, {adult, drama, western}...
9 Multi-label Evaluation For each test example (x i,s i L), classifier H : x Y i L. e.g.: L = {horror,romance,comedy,western,drama,family,... } S i = {romance,comedy,drama} Y i = {romance,comedy} Evaluation by example? S i = Y i? too harsh Evaluation by label? L j l j = k j? l j S i,k j Y i too lenient
10 Multi-label Evaluation For each test example (x i,s i L), classifier H : x Y i L. e.g.: L = {horror,romance,comedy,western,drama,family,... } S i = {romance,comedy,drama} Y i = {romance,comedy} Evaluation by example? S i = Y i? too harsh Evaluation by label? L j l j = k j? l j S i,k j Y i too lenient Combine with other Multi-label Evaluation Metrics: F-measure macro/micro averaged by D / L Subset accuracy One error, Coverage, Rank loss Log loss, Average Area Under the Precision Recall Curve
11 Algorithm Adaption or Problem Transformation Algorithm Adaption Adapt a single-label algorithm for multi-label classification e.g. Multi-label Naive Bayes: Posterior probabilities: H : x P(l 1 ), P(l 2 ),, P(l L ) Classify l j Y where P(l j ) > 0.5 Problem Transformation Transform a multi-label problem into single-label problems Use any single-label classifier for classification Flexible, involved in algorithm adaption anyway e.g. Binary Relevance method, Label Combination method
12 Problem Transformation: Binary Relevance Method (BR) One binary classifier predicts the association of each label L Binary classifiers H 1,,H L, each H j : x l j / l j where all l j Y Example Multi-label example (x, {comedy, romance}) Single-label example (1): (x, horror) Single-label example (2): (x,comedy) Single-label example (3): (x,romance) Single-label example (4): (x, western) Single-label example (.): (x,...) Single-label example ( L )): (x,...) simple, intuitive, fast ignorant of label correlations
13 Problem Transformation: Label Combination Method (LC) Each unique label combination in the training set is treated as a single class-label Example One single-label classifier: H : x Y (x,y ) D Multi-label example: (x,{comedy,romance,western}) Single-label example: (x,comedy+romance+western) Takes into account label correlations 2 L possible combinations
14 Pruned Sets (PS) 1 Problem (Combination Method (LC)): 2 L combinations slow skewed e.g. (x,comedy+romance+western) 1 Jesse Read, Bernhard Pfahringer, Geoff Holmes. Multi-label Classification using Ensembles of Pruned Sets. Proc. of IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 2008.
15 Pruned Sets (PS) 1 Problem (Combination Method (LC)): 2 L combinations slow skewed e.g. (x,comedy+romance+western) Solution (Pruned Sets (PS)): prune and reformat outlying combinations e.g. (x,comedy+romance),(x, western) 1 Jesse Read, Bernhard Pfahringer, Geoff Holmes. Multi-label Classification using Ensembles of Pruned Sets. Proc. of IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 2008.
16 Pruned Sets (PS) 1 Problem (Combination Method (LC)): 2 L combinations slow skewed e.g. (x,comedy+romance+western) Solution (Pruned Sets (PS)): prune and reformat outlying combinations e.g. (x,comedy+romance),(x, western) Expansion (Ensembles of PS (EPS)): form new combinations at classification time i.e. H 1,,H m : x Y 1,,Y m Y L 1 Jesse Read, Bernhard Pfahringer, Geoff Holmes. Multi-label Classification using Ensembles of Pruned Sets. Proc. of IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 2008.
17 Classifier Chains (CC) 2 Problem (Binary Method (BM)): ignorance of label combinations e.g. (x,adult), (x, family), (x, drama),... 2 Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank. Classifier Chains for Multi-label Classification. In Proc. of 20th European Conference on Machine Learning (ECML 2009). Bled, Slovenia, September 2009.
18 Classifier Chains (CC) 2 Problem (Binary Method (BM)): ignorance of label combinations e.g. (x,adult), (x, family), (x, drama),... Solution (Classifier Chains (CC)): chaining mechanism (efficient form of stacking) e.g. (x,adult) (x,?) (x,?) 2 Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank. Classifier Chains for Multi-label Classification. In Proc. of 20th European Conference on Machine Learning (ECML 2009). Bled, Slovenia, September 2009.
19 Classifier Chains (CC) 2 Problem (Binary Method (BM)): ignorance of label combinations e.g. (x,adult), (x, family), (x, drama),... Solution (Classifier Chains (CC)): chaining mechanism (efficient form of stacking) e.g. (x,adult) (x,?) (x,?) Expansion (Ensembles of CC (ECC)): different chain orderings for each model 2 Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank. Classifier Chains for Multi-label Classification. In Proc. of 20th European Conference on Machine Learning (ECML 2009). Bled, Slovenia, September 2009.
20 Related Work Problem Transformataion: Binary pairwise classification e.g. (Fürnkranz et al., 2008) Ensemble LC e.g. (Tsoumakas and Vlahavas, 2007) Meta BR stacking e.g. (Godbole, Sarawagi, 2004) Algorithm Adaption: lazy/knn-based e.g. (Zhang and Zhao, 2007), NN-based, SVM-based e.g. (Elisseeff and Weston, 2002) C4.5-based e.g. (Clare and King, 2001) Boosting e.g. (Schapire and Singer, 2000) Bayesian / probabilistic e.g. (McCallum, 1999)
21 Experiments - Predictive Performance Table: Number of wins over 9 datasets. SVMs used as base classifier for problem transformation methods. Measure ECC EPS CLR 3 RAkEL 4 MLkNN 5 IBLR 6 Subs.acc AU(PRC) LogLoss F1-micro E-match Total CLR: Calibrated Label Ranking (binary pairwise) (Fürnkranz et al., 2008) 4 RAkEL: RAndom k-label subsets (Ens. LC) (Tsoumakas and Vlahavas, 2007) - two parameter configurations 5 MLkNN: knn + Bayes inference (Zhang and Zhou, 2007) 6 IBLR: knn + logistic regression (Cheng and Hüllermeier, 2009)
22 Experiments - Computational Complexity - L BR CC LC RAkEL IBLR(kNN) CLR seconds L Figure: Synthetic data: L = 2, 4,, 256, D = 3000, X = 500. j48 used as the base-classifier for problem transformation methods.
23 Experiments - Computational Complexity - D seconds BR CC LC RAkEL IBLR(kNN) CLR D Figure: Synthetic data: D = 100, 200,, , L = 10, X = 20. j48 used as the base-classifier for problem transformation methods.
24 Efficient Multi-label Classification: Scaling Up large EPS efficient version of LC larger ECC scales up to large datasets largest Can adapt EPS and ECC to incremental settings using e.g. NaiveBayes, HoeffdingTrees as base classifier for very large datasets, data streams
25 Experiments - Very Large Datasets 0.55 EBR ECC EPS EBR ECC EPS oebr oecc oeps Figure: o-eps, o-ecc, o-ebr on different datasets, x axis in thousands. Left: regular labelling: binary methods become skewed. Right: concept drift: combination-based method slow to adapt. Bottom: irregular labelling: label combinations become distracting.
26 How Far has Multi-label Classification Come? Beginning 2007: about 5 good relevant papers 1-3 datasets used in evaluation 1 evaluation measure parameters optimised on test data comparison only to the binary relevance method scalability largely ignored End of 2009: about 5 relevant papers per conference 10+ datasets used in evaluation 4+ evaluation measures various statistical significance tests comparison to a wide range of algorithms scalability to large datasets a hot topic
27 Thank you Thanks for your attention Datasets, Links, MOA-based and WEKA-based Software:
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