Discretization Of Continuous Attributes Research Articles

Rough-Set-Theory-Based Classification with Optimized k-Means Discretization

The discretization of continuous attributes in a dataset is an essential step before the Rough-Set-Theory (RST)-based classification process is applied. There are many methods for discretization, but not many of them have linked the RST instruments from the beginning of the discretization process. The objective of this research is to propose a method to improve the accuracy and reliability of the RST-based classifier model by involving RST instruments at the beginning of the discretization process. In the proposed method, a k-means-based discretization method optimized with a genetic algorithm (GA) was introduced. Four datasets taken from UCI were selected to test the performance of the proposed method. The evaluation of the proposed discretization technique for RST-based classification is performed by comparing it to other discretization methods, i.e., equal-frequency and entropy-based. The performance comparison among these methods is measured by the number of bins and rules generated and by its accuracy, precision, and recall. A Friedman test continued with post hoc analysis is also applied to measure the significance of the difference in performance. The experimental results indicate that, in general, the performance of the proposed discretization method is significantly better than the other compared methods.

Automatic Classification Algorithm for Multisearch Data Association Rules in Wireless Networks

In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real‐time performance, high coverage rate, and high feasibility.

Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

This study develops a tree augmented naive Bayesian (TAN) classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE) in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN) based and multilayer feed forward (MLF) neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

Bayesian network classifiers based on Gaussian kernel density

For learning a Bayesian network classifier, continuous attributes usually need to be discretized. But the discretization of continuous attributes may bring information missing, noise and less sensitivity to the changing of the attributes towards class variables. In this paper, we use the Gaussian kernel function with smoothing parameter to estimate the density of attributes. Bayesian network classifier with continuous attributes is established by the dependency extension of Naive Bayes classifiers. We also analyze the information provided to a class for each attributes as a basis for the dependency extension of Naive Bayes classifiers. Experimental studies on UCI data sets show that Bayesian network classifiers using Gaussian kernel function provide good classification accuracy comparing to other approaches when dealing with continuous attributes.

Discretization of continuous attributes through low frequency numerical values and attribute interdependency

Discretization is the process of converting numerical values into categorical values. There are many existing techniques for discretization. However, the existing techniques have various limitations such as the requirement of a user input on the number of categories and number of records in each category. Therefore, we propose a new discretization technique called low frequency discretizer (LFD) that does not require any user input. There are some existing techniques that do not require user input, but they rely on various assumptions such as the number of records in each interval is same, and the number of intervals is equal to the number of records in each interval. These assumptions are often difficult to justify. LFD does not require any assumptions. In LFD the number of categories and frequency of each category are not pre-defined, rather data driven. Other contributions of LFD are as follows. LFD uses low frequency values as cut points and thus reduces the information loss due to discretization. It uses all other categorical attributes and any numerical attribute that has already been categorized. It considers that the influence of an attribute in discretization of another attribute depends on the strength of their relationship. We evaluate LFD by comparing it with six (6) existing techniques on eight (8) datasets for three different types of evaluation, namely the classification accuracy, imputation accuracy and noise detection accuracy. Our experimental results indicate a significant improvement based on the sign test analysis.

Tuning machine learning algorithms for content-based movie recommendation

Machine learning algorithms are often used in content-based recommender systems since a recommendation task can naturally be reduced to a classification problem: A recommender needs to learn a classifier for a given user where learning examples are characteristics of items previously liked/bought/seen by the user. However, multi-valued and continuous attributes require special approaches for classifier implementation as they can significantly influence classifier accuracy. In this paper we propose novel approaches for handling multi- valued and continuous attributes adequate for the naive Bayes classifier and decision trees classifier, and tune it for content-based movie recommendation. We evaluate the performance of the resulting approaches using the MovieLens data set enriched with movie details retrieved from the Internet Movie Database. Our empirical results demonstrate that the naive Bayes classifier is more suitable for content-based movie recommendation than the decision trees algorithm. In addition, the naive Bayes classifier achieves better results with smart discretization of continuous attributes compared to the approach which models continuous attributes with a Gaussian distribution. Finally, we combine our best performing content-based algorithm with the k-means clustering algorithm typically used for collaborative filtering, and evaluate the performance of the resulting hybrid approach for a movie recommendation task. The experimental results clearly show that the hybrid approach significantly increases recommendation accuracy compared to collaborative filtering while reducing the risk of over specification, which is a typical problem of content-based approaches.

The Discretization of Continuous Attributes Based on Improved SOM Clustering

In order to solve the problem of continuous attribute discretization, a new improved SOM clustering algorithm was proposed. The algorithm uses the SOM to achieve the initial cluster and get the clustering up limit, then treats the initial cluster centers as samples and use the BIRCH hierarchical clustering algorithm to get secondary clustering, then solves the problems of inflated clusters and identifies discrete breakpoints set. Finally, find the nearest neighbors of each cluster center among these any samples of Breakpoints sets which belong to its attribute, and use it as a basis of discrete trimming. The experimental results show that the proposed algorithm outperforms the conventional discrete SOM clustering algorithm in the breakpoints set (contour factor to enhance 75%) and discrete accuracy (incompatible degrees closer to 0) aspects.

A novel approach for discretization of continuous attributes in rough set theory

Discretization of continuous attributes is an important task in rough sets and many discretization algorithms have been proposed. However, most of the current discretization algorithms are univariate, which may reduce the classification ability of a given decision table. To solve this problem, we propose a supervised and multivariate discretization algorithm — SMDNS in rough sets, which is derived from the traditional algorithm naive scaler (called Naive). Given a decision table DT=(U,C,D,V,f), since SMDNS uses both class information and the interdependence among various condition attributes in C to determine the discretization scheme, the cuts obtained by SMDNS are much less than those obtained by Naive, while the classification ability of DT remains unchanged after discretization. Experimental results show that SMDNS is efficient in terms of the classification accuracy and the number of generated cuts. In particular, our algorithm can obtain a satisfactory compromise between the number of cuts and the classification accuracy.

A Fuzzy Clustering Algorithm Based on Parameter Optimization in Large Transaction Database

In view of the discretization of continuous attributes of civil aviation radar intelligence data, this paper proposes a fuzzy partition algorithm of continuous attributes based on weighted index and optimization of clustering number, and its automatic determination of optimal weighted index m and optimal clustering number c overcomes the shortcomings of current attribute fuzzy methods of manual determination of classification number and no consideration of geometry data. The experimental results verify the validity and feasibility of fuzzy attribute discretization of civil aviation radar intelligence data characteristics.

Continuous Attribute Discretization Based on Inflection Point

Continuous attribute discretization plays an important role in data mining. A novel discretization method based on inflection point of curve is presented, which includes calculating the all inflexion points as a candidate discrete point based on the mathematical definition of inflection point, then using decision attribute types of sample as constraint condition, and obtaining the final discrete segment points by Nearest Neighbor Rule. After the actual processing for logging attributes discretization and comparative analysis, the results show that the compatibility of presented discretization method is high in the condition of keeping indiscernibility relation, and its effect is superior to that of the discretization algorithm based on attribute importance.

A new approach for discretizing continuous attributes in learning systems

Discretization is a process to convert continuous attributes into discrete format to represent signals for further data processing in learning systems. The main concern in discretization techniques is to find an optimal representation of continuous values with limited number of intervals that can effectively characterize the data and meanwhile minimize information loss. In this paper, we propose a novel class-attribute interdependency discretization algorithm (termed as NCAIC), which takes account of data distribution and the interdependency between all classes and attributes. In our proposed solution, the upper approximation of rough sets as a prime part of the discretization algorithm is applied, and the class-attribute mutual information is used to automatically control and adjust the scope of the discretization of continuous attributes. Some experiments with comparison to five other discretization algorithms are reported, where 13 benchmarked datasets extracted from UCI database and the well-known C4.5 decision tree tool are employed in this study. Results demonstrate that in general our proposed algorithm outperforms other tested discretization algorithms in terms of classification performance.

Application of Variable Precision Rough Set and Integrated Neural Network to Bearing Fault Diagnosis

The integration of variable precision rough set and neural network is introduced into the bearing fault diagnosis. VPRS-INN fault diagnosis method is proposed: First, utilize the information entropy method for discretization of continuous attributes, and then use attribute dependence degree of the variable precision rough set theory for heuristic reduction. based on the reduction, obtain the optimal decision support system. Finally according to the optimal design system, we design a integrated neural network for fault diagnosis. instances have proved the feasibility and high fault diagnosis rate of the method.

Continuous Attributes Discretization Algorithm based on FPGA

The paper addresses the problem of Discretization of continuous attributes in rough set. Discretization of continuous attributes is an important part of rough set theory because most of data that we usually gain are continuous data. In order to improve processing speed of discretization, we propose a FPGA-based discretization algorithm of continuous attributes making use of the speed advantage of FPGA. Combined attributes dependency degree of rough ret, the discretization system was divided into eight modules according to block design. This method can save much time of pretreatment in rough set and improve operation efficiency. Extensive experiments on a certain fighter fault diagnosis validate the effectiveness of the algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2811

Review on Application of Rough Set Theory

Rough set theory has found an increasingly wide utilization since it was promoted in 1980s.And study on the application of rough set theory in every field has a great development in recent years. Application of rough set theory in attribute reduction, continuous attributes discretization, and uncertainty measuring, as well as application of information entropy in rough set theory are reviewed in this paper. What we will do next is to probe further into the application of information entropy in rough set theory.

A FPGA-Based Discretization Algorithm of Continuous Attributes in Rough Set

Rough set theory is mainly used for analysing, processing fuzzy and uncertain information and knowledge, but most of data that we usually gain are continuous data, rough set theory can pretreat these data and can gain satisfied discretization results. So, discretization of continuous attributes is an important part of rough set theory. Field Programmable Gate Array(FPGA) has been became the mainly platforms that realized design of digital system. In order to improve processing speed of discretization, this paper proposed a FPGA-based discretization algorithm of continuous attributes in rough ret that make use of the speed advantage of FPGA and combined attributes dependency degree. This method could save much time of pretreatment in rough ret and improve operation efficiency.

Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery

The cAnt-Miner algorithm is an Ant Colony Optimization (ACO) based technique for classification rule discovery in problem domains which include continuous attributes. In this paper, we propose several extensions to cAnt-Miner. The main extension is based on the use of multiple pheromone types, one for each class value to be predicted. In the proposed μcAnt-Miner algorithm, an ant first selects a class value to be the consequent of a rule and the terms in the antecedent are selected based on the pheromone levels of the selected class value; pheromone update occurs on the corresponding pheromone type of the class value. The pre-selection of a class value also allows the use of more precise measures for the heuristic function and the dynamic discretization of continuous attributes, and further allows for the use of a rule quality measure that directly takes into account the confidence of the rule. Experimental results on 20 benchmark datasets show that our proposed extension improves classification accuracy to a statistically significant extent compared to cAnt-Miner, and has classification accuracy similar to the well-known Ripper and PART rule induction algorithms.

A UMDA-Based Discretization Method for Continuous Attributes

Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. Univariate Marginal Distribution Algorithm is a modified Evolutionary Algorithms, which has some advantages over classical Evolutionary Algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of Univariate Marginal Distribution Algorithm.The experimental results showed that the proposed method could effectively improve the accuracy of classifier.

OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification

The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means of fuzzy sets, which constitute a fuzzy partition of the domains of these attributes. This method carries out a fuzzy discretization of continuous attributes in two stages. A fuzzy decision tree is used in the first stage to propose an initial set of crisp intervals, while a genetic algorithm is used in the second stage to define the membership functions and the cardinality of the partitions. After defining the fuzzy partitions, we evaluate and compare them with previously existing ones in the literature.

Discretization of Attributes Based on Relative Positive Region of Decision Attribute

Traditional rough set theory can hardly handle the real-life data which contains continuous attribute. In order to solve this problem, a new method for discretization of continuous attributes based on relative positive region of decision attribute is presented. The method which distinguishes from traditional discrete methods firstly gets the relative positive region of decision attribute, and then discrete continuous attributes with the theorem proved in this paper. Finally, the result of an example shows that our method is efficient and feasible.

Data discretization method based on statistical correlation coefficient

Most data mining and induction learning methods can only deal with discrete attributes;therefore,discretization of continuous attributes is necessary.The author proposed a data discretization method based on statistical correlation coefficient.The method captured the interdependence between attributes and target class with the aim to select optimal cut points based on statistical correlation theory.In addition,the author incorporated Variable Precision Rough Set(VPRS) model to effectively control information loss.The proposed method was applied to breast tumor diagnosis and data of other fields.The experimental results show that this method significantly enhances the accuracy of classification of See5.