Discretization Of Attributes Research Articles

A Improved Method of Discretization of Continuous Attributes

Discretization of continuous attributes is one of the important steps in preprocessing of data analysis. In this paper, a new method of supervised discretization of continuous attributes based on entropy and hierarchical clustering guiding by level of consistency of decision table is introduced. This method makes use of the concept of the level of consistency of decision table in Rough Sets. According to the level of consistency of the produced decision table, the number of hierarchical cluster is adjusted dynamically in the first step. And then in the second step, we merge adjacent region based on entropy without damaging the level of consistency. Experiments show that this method is feasible.

MIL: a data discretisation approach

Data discretisation is an important step in the process of machine learning, since it is easier for classifiers to deal with discrete attributes rather than continuous attributes. Over the years, several methods of performing discretisation such as Boolean reasoning, equal frequency binning, entropy have been proposed, explored, and implemented. In this article, a simple supervised discretisation approach called minimum information loss (MIL) is introduced. The prime goal of MIL is to maximise classification accuracy of classifier, minimising loss of information while discretisation of continuous attributes. The performance of the suggested approach is compared with the supervised discretisation algorithms: selective pseudo iterative deletion 4.7 (SPID4.7) and minimum description length principle (MDLP), using four state-of-the-art rule inductive algorithms – neural network, C4.5, Naive-Bayes, and CN2. The empirical results show that the presented approach performs better in several cases in comparison to the other two algorithms.

A New Algorithm of Discretization of Consecutive Attributes Based on Decisions in Rough Sets

Proposes a new domain-knowledge independent algorithm of discretization of consecutive attributes based on decisions to amend the limitation that Rough Sets can only deal with the discrete attributes in data sets. Unlike traditional methods, the candidate breakpoint set is obtained after the calculation and sorting of the attribute significance of each consecutive attribute thus leads to a smaller set size and less computational complexity. At the same time, proposes some rules of reducing candidate breakpoints in order to increase the velocity of system convergence. Using the algorithm, the decision table after discretization will be always consistent and can reserve useful information as much as possible. Finally, the algorithm is knowledge-independent and can be used in different fields without any additional information.

Case Retrieval for Product Configuration Based on NRS and KNN

The accuracy and efficiency of case retrieval decline with individuality and diversity of customer needs in current product configuration design. To cope with this problem, this paper proposes the method of case retrieval based on NRS and KNN. Firstly, setting neighbor value can effectively avoid errors generated by discretization of continuous attributes in classical RS. Secondly, NRS is used for redundant reduction and weight allocation of attributes, and to calculate distance of each sample points by weighted distance. Thirdly, K similar cases are selected according to Euclidean distance with weight, then the case that meet customer needs best is acquired. Lastly, product configuration of coal mining machine is used to illustrate that accuracy and efficiency of the method are superior to the existing methods of case retrieval.

New method for discretization of continuous attributes in rough set theory

A new method for discretization of continuous attributes is put forward to overcome the limitation of the traditional rough sets, which cannot deal with continuous attributes. The method is based on an improved algorithm to produce candidate cut points and an algorithm of reduction based on variable precision rough information entropy. With the guarantee of consistency of decision system, the method can reduce the number of cut points and improve efficiency of reduction. Adopting variable precision rough information entropy as measure criterion, it has a good tolerance to noise. Experiments show that the algorithm yields satisfying reduction results.

An Efficient Method for Discretizing Continuous Attributes

In this paper the authors present a novel method for finding optimal split points for discretization of continuous attributes. Such a method can be used in many data mining techniques for large databases. The method consists of two major steps. In the first step search space is pruned using a bisecting region method that partitions the search space and returns the point with the highest information gain based on its search. The second step consists of a hill climbing algorithm that starts with the point returned by the first step and greedily searches for an optimal point. The methods were tested using fifteen attributes from two data sets. The results show that the method reduces the number of searches drastically while identifying the optimal or near-optimal split points. On average, there was a 98% reduction in the number of information gain calculations with only 4% reduction in information gain.

An Approach for Discretization and Feature Selection Of Continuous-Valued Attributes in Medical Images for Classification Learning

Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization and, identify defining characteristics of the method. We then propose a new supervised approach which combines discretization and feature selection to select the most relevant features which can be used for classification purpose. The classification technique to be used is Associatve Classifiers. The features used are Harlick Texture features extracted from MRI Images. The results show that the proposed method is efficient and well-suited to perform preprocessing of continuous valued attributes.

An Approach for Discretization and Feature Selection Of Continuous-Valued Attributes in Medical Images for Classification Learning

An Approach for Discretization and Feature Selection Of Continuous-Valued Attributes in Medical Images for Classification Learning

A hybrid procedure for extracting rules of production performance in the automobile parts industry

In the manufacturing section, due to limitations of specific resources (e.g., time, people, and equipment), key determinants such as process capacity, human resources supply, and equipment availability may be in uncertain or out-of-control environments, followed by decreasing production performance. Traditionally, earlier studies of related issues of production performance usually used statistical methods for handling these problems. However, these methods become more complex when relationships in the input/output dataset are nonlinear. Furthermore, statistical techniques rely on the restrictive assumption on linear separability, multivariate normality and independence of the predictive variables; unfortunately, many of the common models of production performance violate these assumptions. To remedy these existing shortcomings, the study proposes a hybrid procedure that focuses on the opinions of experts, discretization of decision attributes, and application of well-known artificial intelligent (AI) approaches, such as decision trees (DT), artificial neural networks (ANN), and DT+ANN techniques, for objectively classifying production performance to solve real-world problems that are faced by the automobile parts industry. Two practically collected datasets are employed to verify the proposed hybrid procedure. The experimental results reveal that the proposed hybrid procedure is a good alternative to classify production performance from an intelligent manufacturing perspective objectively. Moreover, the output that is created by the DT C4.5 algorithm is a set of comprehensible and meaningful rules applied readily in knowledge-based performance-evaluating systems for manufacturing managers and HR division managers. The study findings and implications are of value to academicians and practitioners.

A dynamic-programming algorithm for hierarchical discretization of continuous attributes

Discretization techniques can be used to reduce the number of values for a given continuous attribute, and a concept hierarchy can be used to define a discretization of a given continuous attribute. Traditional methods of building a concept hierarchy from a continuous attribute are usually based on the level-wise approach. Unfortunately, this approach suffers from three weaknesses: (1) it only seeks a local optimal solution instead of a global optimal, (2) it is usually subject to the constraint that each interval can only be partitioned into a fixed number of subintervals, and (3) the constructed tree may be unbalanced. In view of these weaknesses, this paper develops a new algorithm based on dynamic-programming strategy for constructing concept hierarchies from continuous attributes. The constructed trees have three merits: (1) they are global optimal trees, (2) each interval is partitioned into the most appropriate number of subintervals, and (3) the trees are balanced. Finally, we carry out an experimental study using real data to show its efficiency and effectiveness.

Integrated method of compromise-based ant colony algorithm and rough set theory and its application in toxicity mechanism classification

Attribute discretization and reduction are two key issues in rough set theory. However, almost all previous studies consider them as two separate steps, which can not capture an inherent relationship between them. In this paper, a bi-objective optimization problem is constructed for simultaneous attribute discretization and reduction. A novel compromise-based ant colony algorithm (CACA) for simultaneously solving attribute discretization and reduction is proposed, which adopts a distance metric to stepwise approach the ideal solution. To improve efficiency of the proposed method, both the cut information and attribute information are adopted to dynamically calculate heuristic information, and a local search strategy is also embedded. The grade of nature spearmint essence (NSE), wine and glass classification problems are used as three test datasets to demonstrate the validity of the proposed CACA. Furthermore, the proposed method is applied to two toxicity mechanism classification problems: the classification of three narcosis mechanisms of aquatic toxicity for 194 organic compounds and the classification of four action modes of 221 phenols. The obtained results illustrate that the proposed method has better prediction performance than linear discriminant analysis, radial basis function neural network and support vector machine.

MODL: A Bayes optimal discretization method for continuous attributes

While real data often comes in mixed format, discrete and continuous, many supervised induction algorithms require discrete data. Efficient discretization of continuous attributes is an important problem that has effects on speed, accuracy and understandability of the induction models. In this paper, we propose a new discretization method MODL1, founded on a Bayesian approach. We introduce a space of discretization models and a prior distribution defined on this model space. This results in the definition of a Bayes optimal evaluation criterion of discretizations. We then propose a new super-linear optimization algorithm that manages to find near-optimal discretizations. Extensive comparative experiments both on real and synthetic data demonstrate the high inductive performances obtained by the new discretization method.

Toward unsupervised correlation preserving discretization

Discretization is a crucial preprocessing technique used for a variety of data warehousing and mining tasks. In this paper, we present a novel PCA-based unsupervised algorithm for the discretization of continuous attributes in multivariate data sets. The algorithm leverages the underlying correlation structure in the data set to obtain the discrete intervals and ensures that the inherent correlations are preserved. Previous efforts on this problem are largely supervised and consider only piecewise correlation among attributes. We consider the correlation among continuous attributes and, at the same time, also take into account the interactions between continuous and categorical attributes. Our approach also extends easily to data sets containing missing values. We demonstrate the efficacy of the approach on real data sets and as a preprocessing step for both classification and frequent itemset mining tasks. We show that the intervals are meaningful and can uncover hidden patterns in data. We also show that large compression factors can be obtained on the discretized data sets. The approach is task independent, i.e., the same discretized data set can be used for different data mining tasks. Thus, the data sets can be discretized, compressed, and stored once and can be used again and again.

Online Discretization of Continuous-Valued Attributes in Rule Induction

Machine learning algorithms designed for engineering applications must be able to handle numerical attributes, particularly attributes with real (or continuous) values. Many algorithms deal with continuous-valued attributes by discretizing them before starting the learning process. This paper describes a new approach for discretization of continuous-valued attributes during the learning process. Incorporating discretization within the learning process has the advantage of taking into account the bias inherent in the learning system as well as the interactions between the different attributes. Experiments have demonstrated that the proposed method, when used in conjunction with the SRI rule induction algorithm developed by the authors, improves the accuracy of the induced model.

Developing soft sensors using hybrid soft computing methodology: a neurofuzzy system based on rough set theory and genetic algorithms

This paper presents a hybrid soft computing modeling approach, a neurofuzzy system based on rough set theory and genetic algorithms (GA). To solve the curse of dimensionality problem of neurofuzzy system, rough set is used to obtain the reductive fuzzy rule set. Both the number of condition attributes and rules are reduced. Genetic algorithm is used to obtain the optimal discretization of continuous attributes. The fuzzy system is then represented via an equivalent artificial neural network (ANN). Because the initial parameter of the ANN is reasonable, the convergence of the ANN training is fast. After the rules are reduced, the structure size of the ANN becomes small, and the ANN is not fully weight-connected. The neurofuzzy approach based on RST and GA has been applied to practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in fluid catalytic cracking unit.

Re-designing Heterogeneous Objects by Attribute Discretization

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Rough sets: current and future developments

Expert SystemsVolume 20, Issue 5 p. 247-250 Free Access Rough sets: current and future developments Bruce Curry, Bruce Curry Cardiff Business SchoolSearch for more papers by this author Bruce Curry, Bruce Curry Cardiff Business SchoolSearch for more papers by this author First published: 10 October 2003 https://doi.org/10.1111/1468-0394.00248Citations: 2AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat References An, A., N. Shan, C. Chan, N. Cercone and W. Ziarko (1996) Discovering rules for water demand prediction: an enhanced rough-set approach, Engineering Applications of Artificial Intelligence, 9 (6), 645– 653. Bazan, J.G. (1998) A comparison of dynamic and non-dynamic rough set methodologies for extracting laws from decision tables, in Rough Sets in Knowledge Discovery 1: Methodology and Applications, L. Polkowski and A. Skowron (eds), Studies in Fuzziness and Soft Computing, Vol. 18, Heidelberg: Physica, Ch. 17, pp. 321– 365. Beynon, M., B. Curry and P. Morgan (2000) Classification and rule induction using rough set theory, Expert Systems, 17 (3), 136– 148. Chmielewski, M.R. and J.W. Grzymala-Busse (1996) Global discretization of continuous attributes as preprocessing for machine learning, International Journal of Approximate Reasoning, 15, 319– 331. Curry, B. (2003) Sampling aspects of rough set theory, Computational Management Science, forthcoming; copy available from the author. Duda, R.O., P.E. Hart and D.G. Stork (2001) Pattern Classification, New York: Wiley. Düntsch, I. and G. Gediga (1997) Statistical evaluation of rough set dependency analysis, International Journal of Human–Computer Studies, 46, 589– 604. Pawlak, Z. (1982) Rough sets, International Journal of Information and Computer Sciences, 11 (5), 341– 356. Pawlak, Z. (2002) Rough sets, decision algorithms and Bayes theorem, European Journal of Operational Research, 136, 181– 189. Slowinski, R. (1993) Rough set learning of preferential attitude in multi-criteria decision making, in Methodologies for Intelligent Systems, J. Komorowski and Z.W. Ras (eds), Lecture Notes in Artificial Intelligence Vol. 689, Berlin: Springer, 642– 651. Skowron, A. and C. Rauszer (1992) The discernibility matrices and functions in information systems, in Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, R. Slowinski (ed.), Dordrecht: Kluwer Academic, 331– 362. Vinterbo, S. and A. Øhrn (2000) Minimal approximate hitting sets and rule templates, International Journal of Approximate Reasoning, 25 (2), 123– 143. Wong, S.K.M., W. Ziarko and R.L. Ye (1986) Comparison of rough-set and statistical methods in inductive learning, International Journal of Man–Machine Studies, 24, 53– 72. Ziarko, W. (1993) Variable precision rough set model, Journal of Computer and System Sciences, 46, 39– 59. Citing Literature Volume20, Issue5November 2003Pages 247-250 ReferencesRelatedInformation

Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation

A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy decision tree. A scheme for automatic linguistic discretization of continuous attributes, based on quantiles, is formulated. A novel concept for measuring the goodness of a decision tree, in terms of its compactness (size) and efficient performance, is introduced. Linguistic rules are quantitatively evaluated using new indices. The rules are mapped to a fuzzy knowledge-based network, incorporating the frequency of samples and depth of the attributes in the decision tree. New fuzziness measures, in terms of class memberships, are used at the node level of the tree to take care of overlapping classes. The effectiveness of the system, in terms of recognition scores, structure of decision tree, performance of rules, and network size, is extensively demonstrated on three sets of real-life data.

Meningitis data mining by cooperatively using GDT-RS and RSBR

This paper describes an application of two rough sets based systems, namely generalized distribution table and rough set (GDT-RS) and rough sets with Boolean reasoning (RSBR) respectively, for mining if–then rules in a meningitis dataset. GDT-RS is a soft hybrid induction system, and RSBR is used for discretization of real valued attributes as a pre-processing step realized before the GDT-RS starts. We argue that discretization of continuous valued attributes is an important pre-processing step in the rule discovery process. We illustrate the quality of rules discovered by GDT-RS is strongly affected by the result of discretization.

Rough approximation by dominance relations

In this article we are considering a multicriteria classification that differs from usual classification problems since it takes into account preference orders in the description of objects by condition and decision attributes. To deal with multicriteria classification we propose to use a dominance-based rough set approach (DRSA). This approach is different from the classic rough set approach (CRSA) because it takes into account preference orders in the domains of attributes and in the set of decision classes. Given a set of objects partitioned into pre-defined and preference-ordered classes, the new rough set approach is able to approximate this partition by means of dominance relations (instead of indiscernibility relations used in the CRSA). The rough approximation of this partition is a starting point for induction of if-then decision rules. The syntax of these rules is adapted to represent preference orders. The DRSA keeps the best properties of the CRSA: it analyses only facts present in data, and possible inconsistencies are not corrected. Moreover, the new approach does not need any prior discretization of continuous-valued attributes. In this article we characterize the DRSA as well as decision rules induced from these approximations. The usefulness of the DRSA and its advantages over the CRSA are presented in a real study of evaluation of the risk of business failure. © 2002 John Wiley & Sons, Inc.