Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.

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An Operational Ontology for the Selection of Advanced Courses in Management Information Systems

International Journal of Engineering Trends and Technology ◽

10.14445/22315381/cati3p225 ◽

2020 ◽

pp. 153-159

Author(s):

Ibraheem M Alharbi

Keyword(s):

Information Systems ◽

Management Information Systems ◽

Management Information ◽

Selection Of ◽

Advanced Courses

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Trust in Management Information Systems (MIS)

Zeitschrift für Arbeits- und Organisationspsychologie A&O ◽

10.1026/0932-4089/a000306 ◽

2020 ◽

Vol 64 (1) ◽

pp. 6-16 ◽

Cited By ~ 5

Author(s):

Sarah M. Meeßen ◽

Meinald T. Thielsch ◽

Guido Hertel

Keyword(s):

Information Systems ◽

Information Overload ◽

Management Information Systems ◽

Empirical Work ◽

Future Research ◽

Management Information ◽

Trust In Management ◽

Actual Use ◽

Trust In Technology ◽

The Internet Of Things

Abstract. Digitalization, enhanced storage capacities, and the Internet of Things increase the volume of data in modern organizations. To process and make use of these data and to avoid information overload, management information systems (MIS) are introduced that collect, process, and analyze relevant data. However, a precondition for the application of MIS is that users trust them. Extending accounts of trust in automation and trust in technology, we introduce a new model of trust in MIS that addresses the conceptual ambiguities of existing conceptualizations of trust and integrates initial empirical work in this field. In doing so, we differentiate between perceived trustworthiness of an MIS, experienced trust in an MIS, intentions to use an MIS, and actual use of an MIS. Moreover, we consider users’ perceived risks and contextual factors (e. g., autonomy at work) as moderators. The introduced model offers guidelines for future research and initial suggestions to foster trust-based MIS use.

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