63 research outputs found
Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies
This paper focuses on the problem of applying rough set theory to mass appraisal. This methodology was first introduced by a Polish mathematician, and has been applied recently as an automated valuation methodology by the author. The method allows the appraiser to estimate a property without defining econometric modeling, although it does not give any quantitative estimation of marginal prices. In a previous paper by the author, data were organized into classes prior to the valuation process, allowing for the if-then, or right “rule” for each property class to be defined. In that work, the relationship between property and class of valued was said to be dichotomic.mass appraisal; property valuation; rough set theory; valued tolerance relation
An Improvement on Extended Kalman Filter for Neural Network Training
Information overload has resulted in difficulties of managing and processing
information. Reduction of data using well-defined techniques such as rough set may
provide a means to overcome this problem. Extracting useful imformation and knowledge from data is a major concern in information science. Artificial intelligence systems, such as neural network systems, are widely used to extract and
infer knowledge from databases. This study explored the training of a neural network inference system using the
extended Kalman filter (EKF) learning algorithm. The inference accuracy, inference duration and training performance of this extended Kalman filter neural network
were compared with the standard back-propagation algorithm and an improved version of the back-propagation neural network learning algorithm. It was
discovered that the extended Kalman filter trained neural network required les
Rough U-Exact Sequence of Rough Groups
The notion of a U-exact sequence is a generalization of the exact sequence. In this paper, we introduce a rough U-exact sequence in a rough group in an approximation space. Furthermore, we provide the properties of the rough U-exact sequence in a rough group
Reduced Data Sets and Entropy-Based Discretization
This work is licensed under a Creative Commons Attribution 4.0 International License.Results of experiments on numerical data sets discretized using two methods—global versions of Equal Frequency per Interval and Equal Interval Width-are presented. Globalization of both methods is based on entropy. For discretized data sets left and right reducts were computed. For each discretized data set and two data sets, based, respectively, on left and right reducts, we applied ten-fold cross validation using the C4.5 decision tree generation system. Our main objective was to compare the quality of all three types of data sets in terms of an error rate. Additionally, we compared complexity of generated decision trees. We show that reduction of data sets may only increase the error rate and that the decision trees generated from reduced decision sets are not simpler than the decision trees generated from non-reduced data sets
Rough Set Applied to Air Pollution: A New Approach to Manage Pollutions in High Risk Rate Industrial Areas
This study presents a rough set application, using together the ideas of classical rough set approach, based on the indiscernibility relation and the dominance-based rough set approach (DRSA), to air micro-pollution management in an industrial site with a high environmental risk rate, such as the industrial area of Syracuse, located in the South of Italy (Sicily). This new data analysis tool has been applied to different decision problems in various fields with considerable success, since it is able to deal both with quantitative and with qualitative data and the results are expressed in terms of decision rules understandable by the decision-maker. In this chapter, some issue related to multi-attribute sorting (i.e. preference-ordered classification) of air pollution risk is presented, considering some meteorological variables, both qualitative and quantitative as attributes, and criteria describing the different objects (pollution occurrences) to be classified, that is, different levels of sulfur oxides (SOx), nitrogen oxides (NOx), and methane (CH4) as pollution indicators. The most significant results obtained from this particular application are presented and discussed: examples of ‘if, … then’ decision rules, attribute relevance as output of the data analysis also in terms of exchangeable or indispensable attributes/criteria, of qualitative substitution effect and interaction between them
Rough set approach for categorical data clustering
A few techniques of rough categorical data clustering exist to group objects
having similar characteristics. However, the performance of the techniques is an
issue due to low accuracy, high computational complexity and clusters purity.
This work proposes a new technique called Maximum Dependency Attributes
(MDA) to improve the previous techniques due to these issues. The proposed
technique is based on rough set theory by taking into account the dependency of
attributes of an information system. The main contribution of this technique is to
introduce a new technique to classify objects from categorical datasets which has
better performance as compared to the baseline techniques.
The algorithm of the proposed technique is implemented in MATLAB®
version 7.6.0.324 (R2008a). They are executed sequentially on a processor Intel Core
2 Duo CPUs. The total main memory is 1 Gigabyte and the operating system is
Windows XP Professional SP3. Results collected during the experiments on four
small datasets and thirteen UCI benchmark datasets for selecting a clustering
attribute show that the proposed MDA technique is an efficient approach in terms of
accuracy and computational complexity as compared to BC, TR and MMR
techniques. For the clusters purity, the results on Soybean and Zoo datasets show that
MDA technique provided better purity up to 17% and 9%, respectively.
The experimental result on supplier chain management clustering also
demonstrates how MDA technique can contribute to practical system and establish
the better performance for computation complexity and clusters purity up to 90% and
23%, respectively
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