A rough set-based association rule approach implemented on exploring beverages product spectrum

[[abstract]]When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if—then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Coevolutionary fuzzy attribute order reduction with complete attribute-value space tree

Since big data sets are structurally complex, high-dimensional, and their attributes exhibit some redundant and irrelevant information, the selection, evaluation, and combination of those large-scale attributes pose huge challenges to traditional methods. Fuzzy rough sets have emerged as a powerful vehicle to deal with uncertain and fuzzy attributes in big data problems that involve a very large number of variables to be analyzed in a very short time. In order to further overcome the inefficiency of traditional algorithms in the uncertain and fuzzy big data, in this paper we present a new coevolutionary fuzzy attribute order reduction algorithm (CFAOR) based on a complete attribute-value space tree. A complete attribute-value space tree model of decision table is designed in the attribute space to adaptively prune and optimize the attribute order tree. The fuzzy similarity of multimodality attributes can be extracted to satisfy the needs of users with the better convergence speed and classification performance. Then, the decision rule sets generate a series of rule chains to form an efficient cascade attribute order reduction and classification with a rough entropy threshold. Finally, the performance of CFAOR is assessed with a set of benchmark problems that contain complex high dimensional datasets with noise. The experimental results demonstrate that CFAOR can achieve the higher average computational efficiency and classification accuracy, compared with the state-of-the-art methods. Furthermore, CFAOR is applied to extract different tissues surfaces of dynamical changing infant cerebral cortex and it achieves a satisfying consistency with those of medical experts, which shows its potential significance for the disorder prediction of infant cerebrum

Front Matter - Soft Computing for Data Mining Applications

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic

Can bank interaction during rating measurement of micro and very small enterprises ipso facto Determine the collapse of PD status?

This paper begins with an analysis of trends - over the period 2012-2018 - for total bank loans, non-performing loans, and the number of active, working enterprises. A review survey was done on national data from Italy with a comparison developed on a local subset from the Sardinia Region. Empirical evidence appears to support the hypothesis of the paper: can the rating class assigned by banks - using current IRB and A-IRB systems - to micro and very small enterprises, whose ability to replace financial resources using endogenous means is structurally impaired, ipso facto orient the results of performance in the same terms of PD assigned by the algorithm, thereby upending the principle of cause and effect? The thesis is developed through mathematical modeling that demonstrates the interaction of the measurement tool (the rating algorithm applied by banks) on the collapse of the loan status (default, performing, or some intermediate point) of the assessed micro-entity. Emphasis is given, in conclusion, to the phenomenon using evidence of the intrinsically mutualistic link of the two populations of banks and (micro) enterprises provided by a system of differential equation