Generalized multigranulation rough sets and optimal granularity selection

Xu, Weihua; Li, Wentao; Zhang, Xiantao

doi:10.1007/s41066-017-0042-9

Cited by 63 publications

(15 citation statements)

References 57 publications

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“…In the future, we will investigate clustering techniques [3,4,5,6,13,19,34] towards decomposing big data into multiple contexts, such that the training data obtained within each cluster can be more representative in the corresponding context. It is also worth to investigate granular computing techniques [7,14,31,35,41,44] for the decomposition of each class in more depth [20,22,24,30], towards further improvements of sample representativeness.…”

Section: Discussionmentioning

confidence: 99%

Subclass-based semi-random data partitioning for improving sample representativeness

Liu

Chen

Cocea

2019

Information Sciences

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In machine learning tasks, it is essential for a data set to be partitioned into a training set and a test set in a specific ratio. In this context, the training set is used for learning a model for making predictions on new instances, whereas the test set is used for evaluating the prediction accuracy of a model on new instances. In the context of human learning, a training set can be viewed as learning material that covers knowledge, whereas a test set can be viewed as an exam paper that provides questions for students to answer. In practice, data partitioning has typically been done by randomly selecting 70% instances for training and the rest for testing. In this paper, we argue that random data partitioning is likely to result in the sample representativeness issue, i.e., training and test instances show very dissimilar characteristics leading to the case similar to testing students on material that was not taught. To address the above issue, we propose a subclass-based semi-random data partitioning approach. The experimental results show that the proposed data partitioning approach leads to significant advances in learning performance due to the improvement of sample representativeness.

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Section: Discussionmentioning

confidence: 99%

Subclass-based semi-random data partitioning for improving sample representativeness

Liu

Chen

Cocea

2019

Information Sciences

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“…Granular selection is one of the important factors affecting the performance of the ESLI scheme [37–39].…”

Section: Methodsmentioning

confidence: 99%

ESLI: Enhancing slope one recommendation through local information embedding

Zhang

et al. 2019

PLoS ONE

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Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error.

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“…Definition 3 (see [30]). Let ℐ = U, A ∪ D, F, G be an information system, X ⊆ U and P = P 1 , P 2 , ⋯, P t ,…”

Section: Preliminariesmentioning

confidence: 99%

A Novel Decision‐Making Approach to Fund Investments Based on Multigranulation Rough Set

Yue

2018

Complexity

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Fund investment is a hot issue in today’s society. How to choose a project for investment is affected by many factors. In view of this problem, this paper starts from the granular computing point of view and combines the multigranulation rough set decision-making method to construct a fund investment decision information system; then, the fund investment decision information system is reduced under different thresholds, and the decision rules are extracted through reduction. And from the aspects of decision accuracy and rule accuracy, the rules are analyzed. Finally, decision rules are used to give the decision of the fund investment project. This study provides a new approach to fund management.

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Generalized multigranulation rough sets and optimal granularity selection

Cited by 63 publications

References 57 publications

Subclass-based semi-random data partitioning for improving sample representativeness

Subclass-based semi-random data partitioning for improving sample representativeness

ESLI: Enhancing slope one recommendation through local information embedding

A Novel Decision‐Making Approach to Fund Investments Based on Multigranulation Rough Set

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