MOEA for discovering Pareto-optimal process models: an experimental comparison

Deshmukh, Sonia; Agarwal, Manoj; Gupta, Shikha; Kumar, Naveen

doi:10.1504/ijcse.2020.106067

Cited by 3 publications

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“…One occurrence of an activity forms an event e. And a sequence of events σ is the trace in the log. Event log L represents a multi-set of traces (Deshmukh et al, 2020). Each time a subsequence appears in the log, we count it one time.…”

Section: Introductionmentioning

confidence: 99%

Discovery of effective infrequent sequences based on maximum probability path

Fang

et al. 2021

Connection Science

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Process discovery usually analyses frequent behaviour in event logs to gain an intuitive understanding of processes. However, there are some effective infrequent behaviours that help to improve business processes in real life. Most existing studies either ignore them or treat them as harmful behaviours. To distinguish effective infrequent sequences from noisy activities, this paper proposes an algorithm to analyse the distribution states of activities and the strong transfer relationships between behaviours based on maximum probability paths. The algorithm divides episodic traces into two categories: harmful and useful episodes, namely noisy activities and effective sequences. First, using conditional probability entropy, the infrequent logs are pre-processed to remove individual noisy activities that are extremely irregularly distributed in the traces. Effective sequences are then extracted from the logs based on the state transfer information of the activities. The algorithm is based on a PM4Py implementation and is validated using synthetic and real logs. From the results, the algorithm not only preserves the key structure of the model and reduces noise activity, but also improves the quality of the model.

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Section: Introductionmentioning

confidence: 99%

Discovery of effective infrequent sequences based on maximum probability path

Fang

et al. 2021

Connection Science

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“…Therefore, we need to find out the Paretooptimal solution for MOO problem that balances the solutions of all the objective functions. Many MOO algorithms are present in the literature that have been used for solving bi-objective problems (Xue et al, 2012;Lai and Xia, 2019;Deshmukh et al, 2020). In this work, the MOO algorithms namely MOEA/D (Zhang and Li, 2007), NSGA-II (Deb et al, 2000), MOABC (Wang et al, 2015b) will be used for feature weighting in order to improve the performance of naïve Bayes classifier.…”

Section: Introductionmentioning

confidence: 99%

Feature weighting for naïve Bayes using multi objective artificial bee colony algorithm

Chaudhuri

Sahu

2021

IJCSE

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Naïve Bayes (NB) is a widely used classifier in the field of machine learning. However, its conditional independence assumption does not hold true in real-world applications. In literature, various feature weighting approaches have attempted to alleviate this assumption. Almost all of these approaches consider the relationship between feature-class (relevancy) and feature-feature (redundancy) independently, to determine the weights of features. We argue that these two relationships are mutually dependent and both cannot be improved simultaneously, i.e., form a trade-off. This paper proposes a new paradigm to determine the feature weight by formulating it as a multi-objective optimisation problem to balance the trade-off between relevancy and redundancy. Multi-objective artificial bee colony-based feature weighting technique for naïve Bayes (MOABC-FWNB) is proposed. An extensive experimental study was conducted on 20 benchmark UCI datasets. Experimental results show that MOABC-FWNB outperforms NB and other existing state-of-the-art feature weighting techniques.

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Online incremental updating for model enhancement based on multi-perspective trusted intervals

Fang

2022

Connection Science

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MOEA for discovering Pareto-optimal process models: an experimental comparison

Cited by 3 publications

References 0 publications

Discovery of effective infrequent sequences based on maximum probability path

Discovery of effective infrequent sequences based on maximum probability path

Feature weighting for naïve Bayes using multi objective artificial bee colony algorithm

Online incremental updating for model enhancement based on multi-perspective trusted intervals

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