Decision Rule Extraction from Trained Neural Networks Using Rough Sets (original) (raw)

A Rough Sets-Based Rule Induction for Numerical Datasets

2018

The design of interpretable classifiers is a major goal in machine learning since many applications rely on a complete understanding of the learning model decisions. Among all interpretable models available in the literature, Rough sets based models have become popular given the capability of rough sets to model imprecise data. Despite its success, some of the most used rough sets models are designed to work for categorical input data. Since this design choice may severely limit the application of such models in real world problems, in this paper, we aim at showing some strategies to enable rough sets based models to work with numerical datasets.

Neural Networks and Rough Sets: A comparative study on data classification

The 2006 International …, 2006

This paper addresses a contrastive study between Neural Networks and Rough Sets on data classification. The experiments were carried out using the Iris database, of public domain, to evaluate the classification. The confusion matrix method was used to evaluate the performance of these classifiers. With these contrastive experiments, we investigated the capacity of each classifier for application in a potential application on knowledge extraction in databases. In this experiment the results indicate that the Neural Networks classifier, except SLP, presents significant superiority on Rough Sets classifiers.

Learning cross-level certain and possible rules by rough sets

Expert Systems with Applications, 2008

Machine learning can extract desired knowledge and ease the development bottleneck in building expert systems. Among the proposed approaches, deriving rules from training examples is the most common. Given a set of examples, a learning program tries to induce rules that describe each class. Recently, the rough-set theory has been widely used in dealing with data classification problems. Most of the previous studies on rough sets focused on deriving certain rules and possible rules on the single concept level. Data with hierarchical attribute values are, however, commonly seen in real-world applications. This paper thus attempts to propose a new learning algorithm based on rough sets to find cross-level certain and possible rules from training data with hierarchical attribute values. It is more complex than learning rules from training examples with single-level values, but may derive more general knowledge from data. Boundary approximations, instead of upper approximations, are

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 EXPERT SYSTEM OF ROUGH SET AND NEURAL NETWORK

1999

The combination of neural network and expert system can accelerate the process of inference, and then rapidly produce associated facts and consequences. However, neural network still has some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. Thus to address these issues we develop a new method for preprocessing based on rough set and merge it with neural network and expert system. The resulting system is a hybrid expert system model called a Hybrid Rough Neural Expert System (HRNES).

Classification and rule induction using rough set theory

Expert Systems, 2000

Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for use in expert systems and to the traditional statistical task of classification. The method is based on a novel classification metric, implemented as upper and lower approximations of a set and more generally in terms of positive, negative and boundary regions. Classification accuracy, which may be set by the decision maker, is measured in terms of conditional probabilities for equivalence classes, and the method involves a search for subsets of attributes (called 'reducts') which do not require a loss of classification quality. To illustrate the technique, RST is employed within a state level comparison of education expenditure in the USA.

The Rough Sets Theory Based Expert Systems

2011

The paper deals with the decision rules synthesis i n the domain of expert systems and knowledge based systems. These systems incorporate expert knowledge which is often expressed in the If Then form. As i t is very hard for experts to formally articulate their knowledge, automated decision rule composing algorithms have been used. The rule composing algorithms are often based on the rough sets theory. Originally, ru les are composed from data table by equivalence relation, while in this paper we investigate the rules based o n dominance relation. The main goal of this paper is to single out possible benefits and advantages of dominance relation based rules over equivalence rel ation based rules.

Knowledge Acquisition under Uncertainty through Rough Set

Knowledge acquisition under uncertainty using rough set theory was first stated as a concept and was introduced by Z.Pawlak in1981. A collection of rules is acquired, on the basis of information stored in a data base-like system, called an information system. Uncertainty implies inconsistencies, which are taken into account, so that the produced are categorized into certain and possible with the help of rough set theory. The approach presented belongs to the class of methods of learning from examples. The taxonomy of all possible expert classifications, based on rough set theory, is also established. It is shown that some classifications are theoretically (and, therefore, in practice) forbidden. For a set of conditions of the information system, and a given action of an expert, lower and upper approximations of a classification, generated are computed in a straightforward way, using their simple definitions. Such approximations are the basis of rough set theory. From these approximations, certain and possible rules may be determined. Certain rules have been propagated separately during the inference process, producing new certain rules. Similarly, possible rules are likely to propagate in a parallel way. Example on the basic of knowledge Acquisition has been discussed in brief.

Zrenjanin , Serbia THE ROUGH SETS THEORY BASED EXPERT SYSTEMS

2011

The paper deals with the decision rules synthesis i n the domain of expert systems and knowledge based systems. These systems incorporate expert knowledge which is often expressed in the If Then form. As i t is very hard for experts to formally articulate their knowledge, automated decision rule composing algori thms have been used. The rule composing algorithms are o ften based on the rough sets theory. Originally, ru les are composed from data table by equivalence relation, w hile in this paper we investigate the rules based o n dominance relation. The main goal of this paper is to single out possible benefits and advantages of dominance relation based rules over equivalence rel ation based rules.

Learning rules from incomplete training examples by rough sets

Expert Systems with Applications, 2002

Machine learning can extract desired knowledge from existing training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete data sets. If some attribute values are unknown in a data set, it is called incomplete. Learning from incomplete data sets is usually more dif®cult than learning from complete data sets. In the past, the rough-set theory was widely used in dealing with data classi®cation problems. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete data sets based on rough sets. A new learning algorithm is proposed, which can simultaneously derive rules from incomplete data sets and estimate the missing values in the learning process. Unknown values are ®rst assumed to be any possible values and are gradually re®ned according to the incomplete lower and upper approximations derived from the given training examples. The examples and the approximations then interact on each other to derive certain and possible rules and to estimate appropriate unknown values. The rules derived can then serve as knowledge concerning the incomplete data set. q