Quickly calculating reduct: An attribute relationship based approach

Rao, Xiansheng; Yang, Xibei; Yang, Xin; Chen, Xiangjian; Liu, Dun; Qian, Yuhua

doi:10.1016/j.knosys.2020.106014

Cited by 36 publications

(3 citation statements)

References 61 publications

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“…The current study offers several theoretical and practical implications for academicians, policymakers, and government entities. First, the current study has uniquely been established on a relationship-based view that postulates that performances can be enhanced based on building good relationships at individual and institution levels (Rao et al, 2020). Hence, government institutions should focus on building a good relationship with the stakeholders, especially with the civil servants holding sensitive positions and are in direct contact with the public and responsible for the decision-making related to the public.…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

The Role of Good governance in Transmitting the Influence of New Public Management and Leadership in Public Servants’ Performance

Istianto¹,

Wahyurudhanto²

2023

Internationally known as Journal of Government and Politics

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Grounded in the relationship-based view, the current study aims to examine the impact of new public management and leadership on the performance of civil servants via an underlying mechanism of good governance practices. Data has been collected from the leaders and officials of civil services in Jakarta, Indonesia, via purposive sampling techniques. Data were analyzed using structural equation modeling in SmartPLS 3.0. Findings revealed the significant influence of the new public management in strengthening the perceptions of good governance among the civil servants, resulting in their higher performances. Likewise, results also revealed the significant influence of public leadership in building positive perceptions of good governance, leading to the increased productivity of the civil servants. The current study is a valuable addition to the existing body of literature by uniquely exploring the impact of new public management and leadership together in enhancing perceptions of good governance among the civil servants utilizing the relationship-based view. Moreover, theoretical and practical implications, along with the future research directions, are discussed in detail.

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Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

The Role of Good governance in Transmitting the Influence of New Public Management and Leadership in Public Servants’ Performance

Istianto¹,

Wahyurudhanto²

2023

Internationally known as Journal of Government and Politics

View full text Add to dashboard Cite

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“…Not only the computations of these information granulations are straightforward because only the distances among samples are required, but also the semantic explanations of these information granulations are clear. For example, the neighborhood based information granulation characterizes the similarity or dissimilarity [38] between any two samples through using only one justifiable parameter, it follows that different parameters offer different scales of such similarity or dissimilarity; the granular ball based information granulation characterizes the similarity or dissimilarity between samples through using the labels of samples, it follows that such mechanism can figure the distribution of data in some degree.…”

Section: Introductionmentioning

confidence: 99%

Triple-G: a new MGRS and attribute reduction

Liu

et al. 2021

Int. J. Mach. Learn. & Cyber.

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Different from classical rough set, Multigranulation Rough Set (MGRS) is frequently designed for approximating target through using multiple results of information granulation. Presently, though many forms of MGRS have been intensively explored, most of them are constructed based on the homogeneous information granulation with respect to different scales or levels. They lack the multi-view which involves the results of heterogeneous information granulation. To fill such a gap, a Triple-G MGRS is developed. Such a Triple-G is composed of three different heterogeneous information granulations:(1) neiGhborhood based information granulation; (2) Gap based information granulation; (3) Granular ball based information granulation. Neighborhood provides a parameterized mechanism while gap and granular ball offer two representative data-adaptive strategies for performing information granulation. Immediately, both optimistic and pessimistic MGRS can be re-constructed. Furthermore, the problem of attribute reduction is also addressed based on the proposed models. Not only the forward greedy searching is used for deriving the Triple-G MGRS related reducts, but also an attribute grouping based accelerator is reported for further speeding up the process of searching reducts. The experimental results over 20 UCI data sets demonstrate the follows: (1) from the viewpoint of the generalization performance, the reducts obtained by our Triple-G MGRS is superior to those obtained by previous researches; (2) attribute grouping does speed up the process of searching reducts.

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“…Attribute reduction [1,2,3,4,5], as a rough set based feature selection technology, has been widely investigated from various perspectives, and it has also been applied to many fields such as pattern recognition [6], decision analysis [7,8,9], data mining [10] and machine learning [11,12,13]. This is mainly because the data collected in real-world applications may contain redundant and irrelative attributes, these attributes may deteriorate the performance of learning algorithms [14,15], attribute reduction can effectively remove these attributes from data through searching a qualified reduct satisfying the intended constraint, and further reduce the dimensionality of data.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Based Multi-Granularity Attribute Reduction: An Acceleration Approach

Jin¹,

Dou²,

Rao³

et al. 2020

Fuzzy Systems and Data Mining VI

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As a feature selection technique in rough set theory, attribute reduction has been extensively explored from various viewpoints especially the aspect of granularity, and multi-granularity attribute reduction has attracted much attention. Nevertheless, it should be pointed out that multiple granularities require to be considered simultaneously to evaluate the significance of candidate attribute in the corresponding process of computing reduct, which may result in high elapsed time of searching reduct. To alleviate such a problem, an acceleration strategy for neighborhood based multi-granularity attribute reduction is proposed in this paper, which aims to improve the computational efficiency of searching reduct. Our proposed approach is actually realized through the positive approximation mechanism, and the processes of searching qualified attributes are executed through evaluating candidate attributes over the gradually reduced sample space rather than all samples. The experimental results over 12 UCI data sets demonstrate that the acceleration strategy can provide superior performance to the naive approach of deriving multi-granularity reduct in the elapsed time of computing reduct without generating different reducts.

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Quickly calculating reduct: An attribute relationship based approach

Cited by 36 publications

References 61 publications

The Role of Good governance in Transmitting the Influence of New Public Management and Leadership in Public Servants’ Performance

The Role of Good governance in Transmitting the Influence of New Public Management and Leadership in Public Servants’ Performance

Triple-G: a new MGRS and attribute reduction

Neighborhood Based Multi-Granularity Attribute Reduction: An Acceleration Approach

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