Classifier Chains for Multi-label Classification
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1 Classifier Chains for Multi-label Classification Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank University of Waikato New Zealand ECML PKDD 2009, September 9, Bled, Slovenia J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
2 Introduction Multi-label Classification Each instance may be associated with multiple labels set of instances X = {x 1,, x m }; set of predefined labels L = {l 1,, l n }; dataset (x 1, S 1 ), (x 2, S 2 ), where each S i L. For example, a film can be labeled {romance,comedy} J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
3 Introduction Multi-label Classification Each instance may be associated with multiple labels set of instances X = {x 1,, x m }; set of predefined labels L = {l 1,, l n }; dataset (x 1, S 1 ), (x 2, S 2 ), where each S i L. For example, a film can be labeled {romance,comedy} Applications Scene, Video classification Text classification Medical classification Biology, Genomics J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
4 Introduction Multi-label Classification Each instance may be associated with multiple labels set of instances X = {x 1,, x m }; set of predefined labels L = {l 1,, l n }; dataset (x 1, S 1 ), (x 2, S 2 ), where each S i L. For example, a film can be labeled {romance,comedy} Applications Scene, Video classification Text classification Medical classification Biology, Genomics Multi-label Issues label correlations: consider {romance,comedy} vs {romance,horror} computational complexity J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
5 Prior Work Binary relevance method (BR): binary problem for each label simple, efficient does not take into account label correlations J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
6 Prior Work Binary relevance method (BR): binary problem for each label simple, efficient does not take into account label correlations Nearest neighbor approaches based on BR, e.g. MLkNN Stacking approaches, e.g. meta level stacking (MS) Pairwise approaches, e.g. calibrated label ranking J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
7 Prior Work Binary relevance method (BR): binary problem for each label simple, efficient does not take into account label correlations Nearest neighbor approaches based on BR, e.g. MLkNN Stacking approaches, e.g. meta level stacking (MS) Pairwise approaches, e.g. calibrated label ranking Label powerset method: label sets are treated as single labels takes into account label correlations computationally complex J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
8 Prior Work Binary relevance method (BR): binary problem for each label simple, efficient does not take into account label correlations Nearest neighbor approaches based on BR, e.g. MLkNN Stacking approaches, e.g. meta level stacking (MS) Pairwise approaches, e.g. calibrated label ranking Label powerset method: label sets are treated as single labels takes into account label correlations computationally complex RAKEL: ensembles of subsets EPS: ensembles of pruned sets J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
9 Prior Work Binary relevance method (BR): binary problem for each label simple, efficient does not take into account label correlations Nearest neighbor approaches based on BR, e.g. MLkNN Stacking approaches, e.g. meta level stacking (MS) Pairwise approaches, e.g. calibrated label ranking Label powerset method: label sets are treated as single labels takes into account label correlations computationally complex RAKEL: ensembles of subsets EPS: ensembles of pruned sets Many other methods take into account label correlations complex, prone to overfitting J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
10 Binary Relevance (BR) L = {romance,horror,comedy,drama,action,western} ( L = 6) Classifiers Classifications C 1 : x {romance,!romance} romance C 2 : x {horror,!horror}!horror C 3 : x {comedy,!comedy} comedy C 4 : x {drama,!drama}!drama C 5 : x {action,!action}!action C 6 : x {western,!western}!western Y L {romance,comedy} J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
11 Binary Relevance (BR) L = {romance,horror,comedy,drama,action,western} ( L = 6) Classifiers Classifications C 1 : x {romance,!romance} romance C 2 : x {horror,!horror}!horror C 3 : x {comedy,!comedy} comedy C 4 : x {drama,!drama}!drama C 5 : x {action,!action}!action C 6 : x {western,!western}!western Y L {romance,comedy} simple, intuitive efficient useful for incremental contexts doesn t account for label correlations J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
12 Classifier Chains (CC) L = {romance,horror,comedy,drama,action,western} ( L = 6) Classifiers Classifications C 1 : x {romance,!romance} romance C 2 : x romance {horror,!horror}!horror C 3 : x romance!horror {comedy,!comedy} comedy C 4 : x romance!horror comedy {drama,!drama}!drama C 5 : x romance!horror comedy!drama!action C 6 : x romance!horror comedy!drama!western Y L = {romance,comedy} J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
13 Classifier Chains (CC) L = {romance,horror,comedy,drama,action,western} ( L = 6) Classifiers Classifications C 1 : x {romance,!romance} romance C 2 : x romance {horror,!horror}!horror C 3 : x romance!horror {comedy,!comedy} comedy C 4 : x romance!horror comedy {drama,!drama}!drama C 5 : x romance!horror comedy!drama!action C 6 : x romance!horror comedy!drama!western Y L = {romance,comedy} similar advantages to binary relevance method time complexity similar in practice takes into account label correlations how to order the chain? J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
14 Ensembles of Classifier Chains (ECC) Ensembles known for augmenting accuracy more label correlations learnt, without overfitting solves chain order issue: each chain has a random order J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
15 Ensembles of Classifier Chains (ECC) Ensembles known for augmenting accuracy more label correlations learnt, without overfitting solves chain order issue: each chain has a random order For i 1 m iterations: L shuffle label set L D subset of training set D train a model CC i given L and D Generic vote/score/threshold method for classification: collect votes from models assign a score to each label apply a threshold to determine relevant labels J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
16 Ensembles of Classifier Chains (ECC) Ensembles known for augmenting accuracy more label correlations learnt, without overfitting solves chain order issue: each chain has a random order For i 1 m iterations: L shuffle label set L D subset of training set D train a model CC i given L and D Generic vote/score/threshold method for classification: collect votes from models assign a score to each label apply a threshold to determine relevant labels Can also be applied to binary relevance method, i.e. EBR J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
17 Experiments WEKA-based framework Support Vector Machines as base classifiers Multi-label datasets: Labels L Instances D 6 Standard Large Multi-label evaluation metrics: accuracy, macro F-measure (label set evaluation) log loss, AU(PRC) (per-label evaluation) build times, test times Method parameters preset to optimise predictive performance (ECC requires no additional parameters) Experiments: 1 Compare Classifier Chains (CC) to the Binary Relevance method (BR) and related BR-based methods. 2 Compare ECC to EBR and modern methods of proven success: RAKEL, EPS, and MLkNN J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
18 Results 1 Comparing CC to BR and related methods SM 1 and MS 2. Table: Standard Datasets: Wins for each evaluation measure. CC BR SM MS Accuracy Macro F Micro F Exact Match Total wins CC s chaining technique justified over default BR CC outperforms other similar methods 1 Subset Mapping: maps output of BR to nearest (Hamming distance) known subset 2 Meta Stacking: stacking the output of BR with meta classifiers J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
19 Results 1 Comparing CC to BR and related methods SM 1 and MS 2. Figure: Standard Datasets: Build times (seconds) CC BM SM MS Scene Yeast Slashdot Medical Enron Reuters CC s complexity comparable to BR except for special cases like Medical (relatively large label set) 1 Subset Mapping: maps output of BR to nearest (Hamming distance) known subset 2 Meta Stacking: stacking the output of BR with meta classifiers J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
20 Results 2 Comparing ECC to EBR and methods: RAKEL 3, EPS 4, and MLkNN 5. Table: Standard Datasets: Wins for each evaluation measure. ECC EBR RAKEL EPS MLkNN Accuracy Macro F Log Loss AU(PRC) Total wins ECC best at per-label prediction (as a binary method) Other methods can sometimes predict better label sets ECC rewarded by conservative prediction (log loss) 3 Tsoumakas and Vlahavas, Read, Pfahringer, Holmes, Zhang and Zhou, 2005 J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
21 Results 2 Comparing ECC to EBR and methods: RAKEL 3, EPS 4, and MLkNN 5. Table: Large Datasets: Wins for each evaluation measure. ECC EBR RAKEL EPS MLkNN Accuracy Macro F Log Loss AU(PRC) Total wins Note: 2 DNF for RAKEL and 1 DNF for EPS. Binary methods are the best choice for large datasets ECC best overall 3 Tsoumakas and Vlahavas, Read, Pfahringer, Holmes, Zhang and Zhou, 2005 J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
22 Results 2 Comparing build and test times between ECC, RAKEL, and EPS. Table: All Datasets: Method with fastest Build,Test time. Dataset Build Test Scene EPS RAK Yeast ECC ECC Slashdot RAK RAK Medical RAK RAK Enron EPS ECC Reuters ECC ECC Dataset Build Test OHSUMED ECC ECC TMC2007 EPS ECC Bibtex ECC ECC MediaMill ECC ECC IMDB RAK ECC Delicious EPS EPS EBR and MLkNN not included ECC s efficiency most noticeable on the larger datasets RAKEL most efficient on smaller datasets EPS can make large gains by pruning, but occasionally too much J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
23 Conclusion Ensembles of Classifier Chains classifier chains improves on the binary relevance method takes into account label correlations without overfitting flexible, efficient performs well, especially on large data sets J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
24 Conclusion Ensembles of Classifier Chains classifier chains improves on the binary relevance method takes into account label correlations without overfitting flexible, efficient performs well, especially on large data sets Thank you. Any questions? J. Read, B. Pfahringer, G. Holmes, E. Frank (UoW) Classifier Chains ECML PKDD / 10
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