Rule Generation Algorithm Research Articles

Association rule mining to detect factors which contribute to heart disease in males and females

This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms – Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders.

A Pareto-based multi-objective evolutionary algorithm for automatic rule generation in network intrusion detection systems

Attacks against computer systems are becoming more complex, making it necessary to continually improve the security systems, such as intrusion detection systems which provide security for computer systems by distinguishing between hostile and non-hostile activity. Intrusion detection systems are usually classified into two main categories according to whether they are based on misuse (signature-based) detection or on anomaly detection. With the aim of minimizing the number of wrong decisions, a new Pareto-based multi-objective evolutionary algorithm is used to optimize the automatic rule generation of a signature-based intrusion detection system (IDS). This optimizer, included within a network IDS, has been evaluated using a benchmark dataset and real traffic of a Spanish university. The results obtained in this real application show the advantages of using this multi-objective approach.

A Fuzzy System Constructed by Rule Generation and Iterative Linear SVR for Antecedent and Consequent Parameter Optimization

This paper proposes a new fuzzy regression model, i.e., the fuzzy system constructed by rule generation and iterative linear support vector regression (FS-RGLSVR) for structural risk minimization. The FS-RGLSVR is composed of Takagi-Sugeno (TS)-type fuzzy if-then rules. These rules are automatically constructed by a self-splitting rule generation algorithm that introduces the self-splitting technique to the k-means clustering algorithm. This new algorithm regards a cluster as a fuzzy rule, where no preassignment of the cluster (rule) number is necessary. The cost function for parameter learning is defined based on structural risk instead of empirical risk minimization in order to achieve generalizability. Tuning all of the free parameters in the FS-RGLSVR using linear support vector regression (SVR) is proposed to minimize the cost function. Each of the consequent and antecedent part parameters is expressed as a linear combination coefficient in a transformed input space so that the linear SVR is applicable. This paper introduces iterative linear SVR to tune antecedent and consequent parameters. This paper demonstrates the capabilities of FS-RGLSVR by two simulated and four practical regression examples. Comparisons with fuzzy systems with different types of learning algorithms verify the performance of the FS-RGLSVR.

A multi-agent solution for reduction of bullwhip effect in fuzzy supply chains

In this paper, we present a new Multi-Agent System for reduction of the bullwhip effect in fuzzy supply chains. First, we show that a supply chain that uses an optimal ordering policy without data sharing among echelons still suffers from the bullwhip effect. Then, we propose the multi-agent solution to manage and reduce the bullwhip effect. The proposed multi-agent system includes four different types of agents in which each agent has its own list of actions. The proposed Multi-agent System applies a new Tabu Search algorithm for fuzzy rule generation, and a new data filtering algorithm for extraction of the bullwhip-free data from supply chain data warehouse. We validate the multi-agent system under different conditions and discuss how the system responds to different factors. The results show that the proposed multi-agent system reduces the bullwhip effect significantly in a rational time.

Role of Certainty Factor in Generating Rough-Fuzzy Rule

The generation of effective feature-based rules is essential to the development of any intelligent system. This paper presents an approach that integrates a powerful fuzzy rule generation algorithm with a rough set-assisted feature reduction method to generate diagnostic rule with a certainty factor. Certainty factor of each rule is calculated by considering both the membership value of each linguistic term introduced at time of fuzzyfication of data as well as possibility values, due to inconsistent data, generated by rough set theory at time of rule generation. In time of knowledge inferencing in an intelligent system, certainty factor of each rule will play an important role to find out the appropriate rule to be selected. Experimental results demonstrate the superiority of our approach.

A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing

This paper proposes a recurrent self-evolving fuzzy neural network with local feedbacks (RSEFNN-LF) for dynamic system processing. A RSEFNN-LF is composed of zero-order or first-order Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy if–then rules. The recurrent structure in a RSEFNN-LF comes from locally feeding the firing strength of a fuzzy rule back to itself. A RSEFNN-LF is constructed on-line via simultaneous structure and parameter learning. In structure learning, an efficient rule and fuzzy set generation algorithm is proposed to generate fuzzy rules on-line and reduce the number of fuzzy sets in each dimension. In parameter learning, the consequent part parameters are learned through a varying-dimensional Kalman filter algorithm whose input dimension varies with structure learning. The antecedent part and feedback loop parameters are learned using a gradient descent algorithm. The RSEFNN-LF is applied to dynamic system identification, chaotic sequence prediction, and speech recognition problems. This paper also compares the performance of the RSEFNN-LF with other recurrent fuzzy neural networks.

3DM: Domain-oriented Data-driven Data Mining

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation

Associative-classification is a promising classification method based on association-rule mining. Significant amount of work has already been dedicated to the process of building a classifier based on association rules. However, relatively small amount of research has been performed in association-rule mining from multi-label data. In such data each example can belong, and thus should be classified, to more than one class. This paper aims at the most demanding, with respect to computational cost, part in associative-classification, which is efficient generation of association rules. This task can be achieved using different frequent pattern mining methods. In this paper, we propose a new method that is based on the state-of-the-art tree-projection-based frequent pattern mining algorithm. This algorithm is modified to improve its efficiency and extended to accommodate the multi-label recurrent-item associative-classification rule generation. The proposed algorithm is tested and compared with A priori-based associative-classification rule generator on two large datasets.

Application of elitist multi-objective genetic algorithm for classification rule generation

We present an elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases. We emphasize on predictive accuracy, comprehensibility and interestingness of the rules. However, predictive accuracy, comprehensibility and interestingness of the rules often conflict with each other. This makes it a multi-objective optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective genetic algorithm with a hybrid crossover operator for optimizing these objectives simultaneously. We have compared our rule discovery procedure with simple genetic algorithm with a weighted sum of all these objectives. The experimental result confirms that our rule discovery algorithm has a clear edge over simple genetic algorithm.

The research of an incremental conceptive clustering algorithm and its application in detecting money laundering

Considering the constantly increasing of data in large databases such as wire transfer database, incremental clustering algorithms play a more and more important role in Data Mining (DM). However, Few of the traditional clustering algorithms can not only handle the categorical data, but also explain its output clearly. Based on the idea of dynamic clustering, an incremental conceptive clustering algorithm is proposed in this paper. Which introduces the Semantic Core Tree (SCT) to deal with large volume of categorical wire transfer data for the detecting money laundering. In addition, the rule generation algorithm is presented here to express the clustering result by the format of knowledge. When we apply this idea in financial data mining, the efficiency of searching the characters of money laundering data will be improved.

Association rule-generation algorithm for mining automotive warranty data

A new association rule-generation algorithm is presented for mining automotive warranty data. The algorithm uses elementary set concept and database manipulation techniques to develop useful relationships between product attributes and causes of failure. These relationships (knowledge) are represented using IF–THEN association rules, where the IF portion of the rule includes set of attributes representing product features (e.g. production date, repair date, mileage-at-repair, transmission, engine type, etc.) and the THEN portion of the rule includes set of attributes that represent decision outcome (e.g. problem-related labor code). Once association rules are developed, the algorithm applies a statistical analysis technique to evaluate the significance of each rule. The rules that pass the significance test are reported in a solution. Application of the association rule-generation algorithm is presented with a data-mining case study from the automotive industry. The knowledge (rules) extracted from the automotive warranty data are used to identify root causes of a particular warranty problem or to develop useful conclusions. Detailed discussion on the source and characteristics of warranty data is also presented.

급성복통과 관련된 지능형 질환 진단시스템을 위한 퍼지 규칙 생성과 이의 최적화

This paper describes knowledge base optimization of an intelligent diagnosis system based on fuzzy relational products(IDS-DAAP) for the diseases with acute abdominal Pain. The knowledge base of IDS-DAAP is composed of the fuzzy rules and the fuzzy membership functions. The author here proposes an advanced intelligent diagnosis system (A-lDS-DAAP) in which the fuzzy rule generation algorithm is applied. Comparing with previous IDS-DAAP and IDS-DAAP-NN, a modified approach with A-IDS-DAAP shows that it improves the diagnosis rate and reduces the time to diagnose.

Rough set-based approach to rule generation and rule induction

During the last decade, databases have been growing rapidly in size and number as a result of rapid advances in database capacity and management techniques. This expansive growth in data and databases has caused a pressing need for the development of more powerful techniques to convert the vast pool of data into valuable information. For the purpose of strategic and decision-making, many companies and researchers have recognized mining useful information and knowledge from large databases as a key research topic and as an opportunity for major revenues and improving competitiveness. In this paper, we will explore a new rule generation algorithm (based on rough sets theory) that can generate a minimal set of rule reducts, and a rule generation and rule induction program (RGRIP) which can efficiently induce decision rules from conflicting information systems. All the methods will also be illustrated with numerical examples.

Neuro-fuzzy rule generation: survey in soft computing framework

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

The methodology of knowledge acquisition from the collection of IR and UV spectra

An expert system for identification of parts of chemical molecules from IR and UV spectra is described. To develop the system, the intelligent knowledge engineering program environment SCANKEE1 (elaborated at the Department of Computer Chemistry) was applied. The expert systems for identification of a chemical compound described in the literature use hand-crafted knowledge bases. To eliminate the time-consuming writing of the rules, a module for the automatic creation of rule knowledge bases was developed. The knowledge derives from the collection of molecular spectra of organic compounds and their structural formulae. The algorithm for the automatic generation of the production rules (in the rule knowledge base) is realized in two steps: (i) the creation of the correlation tables and (ii) the generation of the production rules base.

Algorithm for automatic generation of fuzzy rules applied to power system controllers

A new fuzzy algorithm for the identification of the input-output characteristic of a general system is applied to a power system controller. In particular, the method for the generation of fuzzy rules is applied to a fuzzy logic variable-structure FACTS controller for electrical power system stability improvement. The fuzzy algorithm allows one to obtain a simple analytical form for the input-output characteristic of the identified system. The identification is regarded as a nonlinear optimisation problem solved by means of the quasi-Newton optimisation method. The effectiveness of the proposed approach is verified through numerical simulations.

A Fuzzy Diagnostic Model and Its Application in Automotive Engineering Diagnosis

This paper describes a fuzzy diagnostic model that contains a fast fuzzy rule generation algorithm and a priority rule based inference engine. The fuzzy diagnostic model has been implemented in a fuzzy diagnostic system for the End-of-Line test at automobile assembly plants and the implemented system has been tested extensively and its performance is presented.

R-MINI: An iterative approach for generating minimal rules from examples

Generating classification rules or decision trees from examples has been a subject of intense study in the pattern recognition community, the statistics community, and the machine-learning community of the artificial intelligence area. We pursue a point of view that minimality of rules is important, perhaps above all other considerations (biases) that come into play in generating rules. We present a new minimal rule-generation algorithm called R-MINI (Rule-MINI) that is an adaptation of a well-established heuristic-switching-function-minimization technique, MINI. The main mechanism that reduces the number of rules is repeated application of generalization and specialization operations to the rule set while maintaining completeness and consistency. R-MINI results on some benchmark cases are also presented.

An adaptive fuzzy system for modeling chaos

Most chaotic systems do not have even dynamical behavior over the whole phase space. In some regions, the system stretches and branches more violently than others. In the regions where the dynamics are violent, finer representation must be given. In this paper, we make use of this property to model chaos. We present an adaptive system based on fuzzy logic. It can refine its representation of a region in the phase space if that region requires it. It does so by adaptively generating more fuzzy rules to model a region only if that region has very violent dynamics. Experiments were performed to test the adaptive fuzzy system for capturing the dynamics of a normal dynamical system (the Van der Pol oscillator) as well as two chaotic systems (the Lorenz and Rossler attractors). Results indicate that the fuzzy system can produce an accurate model of the three dynamical systems. The adaptive rule generation algorithm allowed the fuzzy system to have an optimal number of rules.

An equivalence between inductive learning and pseudo-Boolean logic simplification: a rule generation and reduction scheme

To provide confidence in a rule generation and reduction scheme, it is necessary to show an equivalence between at least two alternative approaches that provide the same reduced rule sets. For this purpose we present two rule reduction schemes in fuzzy knowledge based systems. These two schemes are based on: (1) a pseudo-Boolean analysis, and (2) the ID3 algorithm. Here, we show an equivalence between these two rule reduction schemes. As a consequence of this equivalence, we propose to substitute an online and automated expert system shell that uses the ID3 algorithm in place of the manual pseudo-Boolean simplification method for the purposes of rule reduction in fuzzy knowledge bases. For both approaches, we assign pseudo-Boolean variables to the intervals, identified as semantic intervals, on the base-axis of a membership representation. The pseudo-Boolean simplification of disjunctions of conjunctions formulae (DCF) is efficiently learnable from examples in the sense of Valiant (1984, 1985). Positive and negative examples are provided to illustrate that the equivalence holds for all nonnull nodes in decision trees under the condition of extending all the examples to possible attribute combinations. This approach supports an interactive learning environment for the design of fuzzy expert systems where a rule reduction algorithm constitutes an important component of the design process. Another important component is a rule generation algorithm. Two illustrative experiments are provided to demonstrate the rule generation and reduction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>