Neural networks design: Rough set approach to continuous data (original) (raw)

Rough Set Approach in Machine Learning: A Review

International Journal of Computer Applications, 2012

The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction.

New learning models for generating classification rules based on rough set approach

2000

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, fuzz y 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.

Hybrid rough sets/cellular neural networks approach to development of a decision making system

This paper describes a hybrid framework for this kind of switching circuit. In this framework, cellular neural networks (CNN) and rough sets are integrated into a hybrid system and used cooperatively during the system lifecycle. Rough sets and CNN were chosen for this application because they can discover patterns in ambiguous and imperfect data and provide tools for data and pattern analysis.

Rough Set Approaches to Unsupervised Neural Network Based Pattern Classifier

Lecture Notes in Electrical Engineering, 2009

Early Convergence, input feature space with minimal dimensions and good classification accuracy are always the most desired characteristics of an unsupervised neural network based pattern classifier. To achieve these, various approaches comprising of various soft computing tools can be used at different levels of implementations of such classifiers. Rough set is also one such tool, which can be used at either preprocessing level or learning level or architectural implementation level. Approaches using rough sets at first and the third levels are discussed here. Use of rough sets at the above stated levels result in dimensionality reduction of feature space through preprocessing and early convergence through rough neuron.

Design of rough neurons: Rough set foundation and Petri net model

2010

This paper introduces the design of rough neurons based on rough sets. Rough neurons instantiate approximate reasoning in assessing knowledge gleaned from input data. Each neuron constructs upper and lower approximations as an aid to classifying inputs. The particular form of rough neuron considered in this paper relies on what is known as a rough membership function in assessing the accuracy of a classification of input signals. The architecture of a rough neuron includes one or more input ports which filter inputs relative to selected bands of values and one or more output ports which produce measurements of the degree of overlap between an approximation set and a reference set of values in classifying neural stimuli. A class of Petri nets called rough Petri nets with guarded transitions is used to model a rough neuron. An application of rough neural computing is briefly considered in classifying the waveforms of power system faults. The contribution of this article is the presentation of a Petri net model which can be used to simulate and analyze rough neural computations.

Towards rough neural computing based on rough membership functions: Theory and application

2001

This paper introduces a neural network architecture based on rough sets and rough membership functions. The neurons of such networks instantiate approximate reasoning in assessing knowledge gleaned from input data. Each neuron constructs upper and lower approximations as an aid to classifying inputs. Rough neuron output has various forms. In this paper, rough neuron output results from the application of a rough membership function. A brief introduction to the basic concepts underlying rough membership neural networks is given.

A new Enhanced Automated Fuzzy-Based Rough Decision Model

In this paper, we introduce a new automated Fuzzy-Based Rough Decision Model algorithm. Our Algorithm consists of three phases are: () automatic attributes fuzzification, () eliminate redundant attributes using rough set theory, () Generating Fuzzy rough rules then calculate automatically fitness value (Confidence) and support for each rule. In phase one, the user input the number of fuzzy sets of each attributes, our algorithm determine the maximum and minimum values of each attribute that define and calculates automatically the width (∆) which divides the universe of discourse of each attribute into " n " intervals according to the number of fuzzy sets, also the algorithm calculates automatically the width (δ i) according to the width (∆). In phase two, we use the rough set techniques to reduce the number of attributes that comes from phase one and produce fuzzy-rough rules. In phase three, our algorithm automatically calculates the confidence (weight or fitness value) and support of each fuzzy rough rule then it calculates the total weight or fitness value of all linguistic rules. The result of fitness value of our algorithm that applied on dataset in (X.Hu, T.Lin, and J.Han.) [ ] is. Rough sets theory was first introduced by Pawlak in the s [ , , , ] and it has been applied in many applications such as machine learning, knowledge discovery, and expert systems since then. It provides powerful tools for data analysis and data mining from imprecise and ambiguous data. Many rough sets models have been developed in the rough set community in the last decades [ , , ]. Applying the traditional rough set models in large data sets in data mining applications has shown that one of the strong drawbacks of the classical rough set theory assumes that all attributes values are discrete. In real life datasets values of attributes could be both of symbolic and real-valued. Therefore, the traditional rough set theory will have difficulty in handling such values. There is a need for some methods which have the capability of utilizing set approximations and attributes reduction for real-valued attributed dataset. This can be done by combining (integrate) fuzzy sets and rough sets in a Fuzzy-Based Rough Model [ , , , , , ]. Another major drawback of traditional Fuzzy-Based Rough Model is that the linguistic values (fuzzy sets) for numeric values of each attribute should determining by the membership functions of these linguistic terms which, every element in the universe of discourse is a member of the fuzzy set with some grade (degree of membership functions). Therefore, the user should define the parameters of membership functions of these linguistic values from his view which is different from one user to another. Therefore, we propose a new automated Fuzzy-Based Rough Decision Model algorithm that can define the parameters of membership functions of these linguistic values automatically that the user determine only the number of fuzzy sets (linguistic values) then the maximum and minimum values of each attribute are determined automatically then the algorithm calculates the width (∆) that divides the universe of discourse " u " of each attribute into " n " intervals according to the number of fuzzy sets then the algorithm calculates automatically the width (δi) according to the width (∆). Another strong drawback of the traditional rough set theory is the inefficiency of rough set methods and algorithms of computing the core attributes and reduct and identifying the dispensable attributes, which limits the suitability of the traditional rough set model in data mining applications. Further investigation of the problem reveals that most existing rough set models [ , , , ] do not integrate with the relational database systems and a lot of computational intensive operations are performed in flat files rather than utilizing the high performance database set operations. Moreover, not much attention and attempt have been paid to design new rough sets model by effectively combining database technologies to generate the core attributes and reducts so as to make their computations efficient and scalable in the large data set. To overcome this problem, a New Rough Sets Model Based on Database Systems has been introduced [ , ] for this purpose to redefine some concepts of rough set theory such as core attributes and reducts by using relational algebra so that the computation of core attributes and reducts can be performed with very efficient set-oriented database operations, such as Cardinality to denote the count and Projection. The paper is organized as follows: We give an overview of the rough set theory based on the model proposed by Pawlak [ , ] with some examples in Section. In Section , we give an

Dimensionality Reduction Using Rough Set Approach for Two Neural Networks-Based Applications

Rough Sets and Intelligent Systems Paradigms, 2007

In this paper, Rough Sets approach has been used to reduce the number of inputs for two neural networks-based applications that are, diagnosing plant diseases and intrusion detection. After the reduction process, and as a result of decreasing the complexity of the classifiers, the results obtained using Multi-Layer Perceptron (MLP) revealed a great deal of classification accuracy without affecting the classification decisions.

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

Lahore Garrison University Research Journal of Computer Science and Information Technology, 2021

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tr...

A generic scheme for generating prediction rules using rough sets

Studies in Computational Intelligence, 2009

This chapter presents a generic scheme for generating prediction rules based on rough set approach for stock market prediction. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. For comparison, the results obtained using rough set approach were compared to that of artificial neural networks and decision trees. Empirical results illustrate that Rough set approach have a higher overall prediction accuracy reaching over 97% and generates more compact and fewer rules than neural networks and Decision tree algorithm.