Discriminative Frequent Pattern Analysis for Effective Classification

By Hong Cheng, Xifeng Yan, Jiawei Han, Chih-Wei Hsu. 2007

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Boolean Decision Rules via Column Generation (2018, background): “see also the survey [30]) of learning rules one by one and removing “covered” examples, a bottomup strategy of combining more specific rules into more general ones [45, 22, 41], and associative classification in which association rule mining is followed by rule selection using various criteria [38, ”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “The resulting ordered rules are instances of decision lists [10, 11, 13, 45, 52, 56].”
Learning Optimized Or's of And's (2015, methodology): “, 1989), the algorithms designed for inductive logic programming that essentially produce DNF (Muggleton and De Raedt, 1994; Lavrac and Dzeroski, 1994), the associative classification algorithms (Ma, Liu, and Hsu, 1998; Han, Pei, and Yin, 2000; Li, Han, and Pei, 2001; Yin and Han, 2003; Chen et al.,”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, methodology): “Some of these methods, like CBA, CPAR and CMAR [10, 11, 26, 51] still suffer from a huge number of patterns and do not yield interpretable classifiers, yet it is well-known that for many domains, the space of good predictive models is often large enough to include very simple models [20].”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Then we use information gain to screen rules, similarly to other works (Chen et al., 2006; Cheng et al., 2007).”
Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation (2014, neutral): “Following the entropy-based approach in [25], and a high value of D(t) implies that the pattern t occurs only in very few actions; O(t) is the representativity score for a pattern t and it considers the divergence between the optimal distribution for class c p(A|tc) and the distribution for pattern ”
Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, methodology): “Unlike its predecessors that use heuristic strategies for feature selection (such as [7]) or do not provide optimality guarantees, we define a principled, near-optimal approach to feature selection on frequent subgraphs that can be integrated into gSpan [30].”
TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015, methodology): “In this work, we also adopt the idea of graph transfer but focus on non-streaming scenarios for cross-network node classification.”
Weighted Substructure Mining for Image Analysis (2007, background): “[1] recently showed under moderate assumptions that frequent patterns are more likely to be discriminative for telling apart samples of different classes.”
Correlated itemset mining in ROC space: a constraint programming approach (2009, neutral): “A large number of publications is concerned with correlated pattern mining [21, 24, 22], but the problem is also known under the names of interesting itemset mining [3], contrast set mining [1], emerging itemset mining [12], subgroup discovery [26, 18, 16] and discriminative itemset mining [8, 9, 27”
Exploring discriminative pose sub-patterns for effective action classification (2013, background): “[8, 9] achieved this by feature combination, namely, mapping the original features into a higher dimensional space by combing the frequent patterns with the original single features.”
Mining significant graph patterns by leap search (2008, background): “[5] derived a frequency upper bound of discriminative measures such as information gain and Fisher score, showing a relationship between frequency and discriminative measures.”
Mining Mid-level Features for Image Classification (2014, methodology): “This is surprising, since it has been shown that these mining methods allow the construction of high-level sets of compound features which can, in many cases, capture more discriminative information (Cheng et al. 2007).”
A discriminative model approach for accurate duplicate bug report retrieval (2010, background): “The other interesting direction is adopting pattern-based classiﬁcation [7, 13] to extract richer feature set that enables better discrimination and detection of duplicate bug reports.”
Robustness in Coreference Resolution (2020, methodology): “In this regard, frequent patterns have been introduced as promising candidates to be used as features that can result in more accurate models (Cheng et al., 2007).”
CBC: An associative classifier with a small number of rules (2014, methodology): “Similar data transform techniques were used for a set of rules [2] (where the focus was to organize/prune rules graphically), and a set of patterns [6].”