LEM2-Based Rule Induction from Data Tables with Imprecise Evaluations

Inuiguchi, Masahiro; Tsuji, Masahiko; Kusunoki, Yoshifumi; Tsurumi, Masayo

doi:10.1007/978-3-642-24425-4_28

Cited by 2 publications

(9 citation statements)

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“…Using vp(u) and 7rp(u), the following underlying object sets have been considered for rule induction (see [7]): i.e., …”

Section: Tablesmentioning

confidence: 99%

“…He showed that four types of rules can be induced from imprecise decision tables, and proposed decision matrix-based rule induction methods. Inuiguchi and Tsuji [7] inherited this idea and proposed LEM2-based rules induction methods from imprecise decision tables. The treatments of imprecise evaluations are different be tween the first two papers [5], [6] and the last paper [7].…”

Section: Introductionmentioning

confidence: 98%

“…Inuiguchi and Tsuji [7] inherited this idea and proposed LEM2-based rules induction methods from imprecise decision tables. The treatments of imprecise evaluations are different be tween the first two papers [5], [6] and the last paper [7]. Several imprecise evaluations from different sources are aggregated into a set of decision attribute values in the former approach [5], [6] while they are aggregated twofold sets, i.e., presumable decision attribute values and possible decision attribute values in the latter approach [7].…”

Section: Introductionmentioning

confidence: 98%

“…The treatments of imprecise evaluations are different be tween the first two papers [5], [6] and the last paper [7]. Several imprecise evaluations from different sources are aggregated into a set of decision attribute values in the former approach [5], [6] while they are aggregated twofold sets, i.e., presumable decision attribute values and possible decision attribute values in the latter approach [7]. Moreover, Inuiguchi [6] and Inuiguchi and Tsuji [7] have defined object sets which are suitable for rule induction schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Several imprecise evaluations from different sources are aggregated into a set of decision attribute values in the former approach [5], [6] while they are aggregated twofold sets, i.e., presumable decision attribute values and possible decision attribute values in the latter approach [7]. Moreover, Inuiguchi [6] and Inuiguchi and Tsuji [7] have defined object sets which are suitable for rule induction schemes. Corresponding to a rule induction scheme, an attribute reduction scheme can be defined.…”

Section: Introductionmentioning

confidence: 99%

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Attribute reduction for imprecise decision tables

Inuiguchi

2012

2012 IEEE International Conference on Granular Computing

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In this paper, we investigate approaches to at tribute reduction for decision tables with imprecise decision attribute values. First, we introduce imprecise decision ta bles, and presumable and possible decision attribute value sets. We define several meaningful object sets based on the twofold decision attribute value sets. Using those object sets, we propose value-oriented and object-oriented approaches to attribute reduction of imprecise decision tables. We show some properties of several reducts defined by the approaches. These properties help the selection of reducts suitable for the problem and analyst preference. A numerical example is given.

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“…Using vp(u) and 7rp(u), the following underlying object sets have been considered for rule induction (see [7]): i.e., …”

Section: Tablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Attribute reduction for imprecise decision tables

Inuiguchi

2012

2012 IEEE International Conference on Granular Computing

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Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection System

Pękala

Mroczek

Gil

et al. 2022

Sensors

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Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of human activity, is expected to expand. This research focuses on improving the solution of the posture detection problem, which is a part of fall detection system. Fall detection, using depth maps obtained by the Microsoft Kinect sensor, is a two-stage method. We concentrate on the first stage of the system, that is, pose recognition from a depth map. For lying pose detection, a new hybrid FRSystem is proposed. In the system, two rule sets are investigated, the first one created based on a domain knowledge and the second induced based on the rough set theory. Additionally, two inference aggregation approaches are considered with and without the knowledge measure. The results indicate that the new axiomatic definition of knowledge measures, which we propose has a positive impact on the effectiveness of inference and the rule induction method reducing the number of rules in a set maintains it.

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LEM2-Based Rule Induction from Data Tables with Imprecise Evaluations

Cited by 2 publications

References 5 publications

Attribute reduction for imprecise decision tables

Attribute reduction for imprecise decision tables

Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection System

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