Multi-instance multi-label learning

Zhou, Zhi‐Hua; Zhang, Min-Ling; Huang, Sheng-Jun; Li, Yufeng

doi:10.1016/j.artint.2011.10.002

Cited by 365 publications

(252 citation statements)

References 54 publications

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“…MIMLSVM [27] and MIMLFast [11] are also compared and listed. The offline data are the log records, which are consistent with Sect.…”

Section: Recommendation Back Test On Offline Datamentioning

confidence: 99%

“…Therefore, we test our solutions to the state-of-the-art MIML approaches, i.e., MIMLSVM [27] and MIMLfast, on the real operational data. These data sets are extracted from the event log records of three different smart phone games, i.e., Three Kingdoms, Rock Em Blocks, and Parkour.…”

Section: Classification On Real Operational Datamentioning

confidence: 99%

“…↑ (↓) Indicates that the larger (smaller) the evaluation value is, the better the performances are pared our three variational solutions with MIMLfast rather than all state-of-the-arts approaches on these benchmarks. The performances are evaluated with five commonly used MIML criteria, i.e., Hamming Loss, One Error, Coverage, Ranking Loss, and Average Precision [27]. For the first four criteria, the smaller the better, while for Avg.…”

Section: Classification On Benchmark Data Setsmentioning

confidence: 99%

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Online game props recommendation with real assessments

Zhan

Tang

Zhou

2016

Complex Intell. Syst.

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With the rapid development of smart mobile devices, phone games become an important way of entertainments. Benefitting from sophisticated payment environments of mobile platforms, e.g., Apple APP store, the In-APP purchases which sell equipments or virtual props bring in the main profits for game carriers and developers. Although virtual props from a certain type of smart phone game are monopolized by only one seller, like other commodities, products' recommendation is able to improve the profit margins as well. One main difference between virtual props recommendation and the general good recommendations lies in that the virtual props are closely related to the game contexts, and this will lead to complicated dependencies. Therefore, general recommendation systems without consideration on game contexts cannot perform very well. Besides, multiple types of props in one game may depend on different game characters of players, thus single player trends to buy only appropriate props for improving the skills of his game characters. Moreover, the purchase intensions of players are influenced by multiple factors, and will change over time. Therefore, it is desired recommendation approach to be capable of handling the role dependencies and concept variations. In this paper, we treat the game contexts as events from game log records, and model the game props recommendation into a multi-instance multi-label learning task for utilizing the complicated dependencies and capturing the rank of purchase intentions. We proposed three variants of solutions against the concept variation problem as well. Finally, we conduct comprehensive empirical investigations on real-world data sets and a series of real online smart phone games. The positive experiment results and increasing profit margins validate the remarkable effectiveness of our solutions.

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“…MIMLSVM [27] and MIMLFast [11] are also compared and listed. The offline data are the log records, which are consistent with Sect.…”

Section: Recommendation Back Test On Offline Datamentioning

confidence: 99%

Section: Classification On Real Operational Datamentioning

confidence: 99%

Section: Classification On Benchmark Data Setsmentioning

confidence: 99%

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Online game props recommendation with real assessments

Zhan

Tang

Zhou

2016

Complex Intell. Syst.

Self Cite

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“…In literature, often, problems that are connected with multilabel data are considered for classification: multi-label learning [14], multi-instance learning [9] etc. In multi-label learning, the output for each instances can be a set of decisions, whereas in our framework, we chose only one decision as output for each instance.…”

Section: Related Workmentioning

confidence: 99%

“…Thus we have objects with equal values of conditional attributes but with different decisions. In literature, often, decision trees and other classifiers for multi-label data are considered for prediction (multi-label classification problem) [6], [7], [8], [9]. However, in this paper our aim is to study decision trees for multi-label decision tables for knowledge representation, and as algorithms for problem solving.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Size of Decision Trees for Multi-label Decision Tables

Azad¹,

Moshkov²

2014

Annals of Computer Science and Information Systems

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Abstract-We used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).

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Cost‐effective multi‐instance multilabel active learning

Zhou

2021

Int J of Intelligent Sys

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Multi-instance multi-label (MIML) Active Learning (M2AL) aims to improve the learner while reducing the cost as much as possible by querying informative labels of complex bags composed of diverse instances. Existing M2AL solutions suffer high query costs for scrutinizing all relevant labels of MIML samples, querying excessive bag-label or instance-label pairs. To address these issues, a Cost-effective M2AL solution (CM2AL) is presented. CM2AL first selects the most informative bag-label pairs by leveraging uncertainty, label correlations, label space sparsity, and informativeness from queried instances of the bag, and thus avoids scrutinizing all labels. Next, it queries the most probably positive instance-label pairs of the selected bag-label pair.Particularly, if the feedback is positive, the bag is positively annotated with the label. For negative feedback, it further leverages the label of the neighborhood bags and the label of the nearby instances of queried instances of this bag, if the suggested labels from bag-and instancelevels disagree, CM2AL temporally gives up querying this bag-label pair and moves to another most informativeness one; otherwise, it takes the agreed label to annotate the bag, which further saves the cost by avoiding the excessive query. Extensive experiments on MIML data sets from diverse domains show that CM2AL can more reduce the cost while managing a better performance than state-of-the-art methods, the collaboration between bags and instances contributes to the saved cost.

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Multi-instance multi-label learning

Cited by 365 publications

References 54 publications

Online game props recommendation with real assessments

Online game props recommendation with real assessments

Minimizing Size of Decision Trees for Multi-label Decision Tables

Cost‐effective multi‐instance multilabel active learning

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