2,628 research outputs found
New Learning Models for Generating Classification Rules Based on Rough Set Approach
Data sets, static or dynamic, are very important and useful for presenting real life
features in different aspects of industry, medicine, economy, and others. Recently,
different models were used to generate knowledge from vague and uncertain data
sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm,
rough set theory, and others. All of these models take long time to learn for a huge
and dynamic data set. Thus, the challenge is how to develop an efficient model that
can decrease the learning time without affecting the quality of the generated
classification rules. Huge information systems or data sets usually have some
missing values due to unavailable data that affect the quality of the generated
classification rules. Missing values lead to the difficulty of extracting useful
information from that data set. Another challenge is how to solve the problem of
missing data. Rough set theory is a new mathematical tool to deal with vagueness and uncertainty.
It is a useful approach for uncovering classificatory knowledge and building a
classification rules. So, the application of the theory as part of the learning models
was proposed in this thesis.
Two different models for learning in data sets were proposed based on two different
reduction algorithms. The split-condition-merge-reduct algorithm ( SCMR) was
performed on three different modules: partitioning the data set vertically into subsets,
applying rough set concepts of reduction to each subset, and merging the reducts of
all subsets to form the best reduct. The enhanced-split-condition-merge-reduct
algorithm (E SCMR) was performed on the above three modules followed by another
module that applies the rough set reduction concept again to the reduct generated by
SCMR in order to generate the best reduct, which plays the same role as if all
attributes in this subset existed. Classification rules were generated based on the best
reduct.
For the problem of missing data, a new approach was proposed based on data
partitioning and function mode. In this new approach, the data set was partitioned
horizontally into different subsets. All objects in each subset of data were described
by only one classification value. The mode function was applied to each subset of
data that has missing values in order to find the most frequently occurring value in
each attribute. Missing values in that attribute were replaced by the mode value.
The proposed approach for missing values produced better results compared to other
approaches. Also, the proposed models for learning in data sets generated the classification rules faster than other methods. The accuracy of the classification rules
by the proposed models was high compared to other models
Efficient schemes on solving fractional integro-differential equations
Fractional integro-differential equation (FIDE) emerges in various modelling of
physical phenomena. In most cases, finding the exact analytical solution for FIDE is
difficult or not possible. Hence, the methods producing highly accurate numerical
solution in efficient ways are often sought after. This research has designed some
methods to find the approximate solution of FIDE. The analytical expression of
Genocchi polynomial operational matrix for left-sided and right-sided Caputo’s
derivative and kernel matrix has been derived. Linear independence of Genocchi
polynomials has been proved by deriving the expression for Genocchi polynomial
Gram determinant. Genocchi polynomial method with collocation has been
introduced and applied in solving both linear and system of linear FIDE. The
numerical results of solving linear FIDE by Genocchi polynomial are compared with
certain existing methods. The analytical expression of Bernoulli polynomial
operational matrix of right-sided Caputo’s fractional derivative and the Bernoulli
expansion coefficient for a two-variable function is derived. Linear FIDE with mixed
left and right-sided Caputo’s derivative is first considered and solved by applying the
Bernoulli polynomial with spectral-tau method. Numerical results obtained show that
the method proposed achieves very high accuracy. The upper bounds for th
A DISTANCE BASED INCREMENTAL FILTER-WRAPPER ALGORITHM FOR FINDING REDUCT IN INCOMPLETE DECISION TABLES
Tolerance rough set model is an effective tool for attribute reduction in incomplete decision tables. In recent years, some incremental algorithms have been proposed to find reduct of dynamic incomplete decision tables in order to reduce computation time. However, they are classical filter algorithms, in which the classification accuracy of decision tables is computed after obtaining reduct. Therefore, the obtained reducts of these algorithms are not optimal on cardinality of reduct and classification accuracy. In this paper, we propose the incremental filter-wrapper algorithm IDS_IFW_AO to find one reduct of an incomplete desision table in case of adding multiple objects. The experimental results on some sample datasets show that the proposed filter-wrapper algorithm IDS_IFW_AO is more effective than the filter algorithm IARM-I [17] on classification accuracy and cardinality of reduc
Geometric lattice structure of covering and its application to attribute reduction through matroids
The reduction of covering decision systems is an important problem in data
mining, and covering-based rough sets serve as an efficient technique to
process the problem. Geometric lattices have been widely used in many fields,
especially greedy algorithm design which plays an important role in the
reduction problems. Therefore, it is meaningful to combine coverings with
geometric lattices to solve the optimization problems. In this paper, we obtain
geometric lattices from coverings through matroids and then apply them to the
issue of attribute reduction. First, a geometric lattice structure of a
covering is constructed through transversal matroids. Then its atoms are
studied and used to describe the lattice. Second, considering that all the
closed sets of a finite matroid form a geometric lattice, we propose a
dependence space through matroids and study the attribute reduction issues of
the space, which realizes the application of geometric lattices to attribute
reduction. Furthermore, a special type of information system is taken as an
example to illustrate the application. In a word, this work points out an
interesting view, namely, geometric lattice to study the attribute reduction
issues of information systems
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