Rule based systems

Knowledge mined from clinical data can be used for medical diagnosis and prognosis. By improving the quality of knowledge base, the efficiency of prediction of a knowledge-based system can be enhanced. Designing accurate and precise clinical decision support systems, which use the mined knowledge, is still a broad area of research. This work analyses the variation in classification accuracy for such knowledge-based systems using different rule lists. The purpose of this work is not to improve the prediction accuracy of a decision support system, but analyze the factors that influence the efficiency and design of the knowledge base in a rule-based decision support system. Three benchmark medical datasets are used. Rules are extracted using a supervised machine learning algorithm (PART). Each rule in the ruleset is validated using nine frequently used rule interestingness measures. After calculating the measure values, the rule lists are used for performance evaluation. Experimental r...

This article tries to explain our rule-based Arabic Named Entity recognition (NER) and classification system. It is based on lists of classified proper names (PN) and particularly on syntactico-semantic
patterns resulting in fine classification of Arabic NE. These patterns use syntactico-semantic combination of morpho-syntactic and syntactic entities. It also uses lexical classification of trigger words and NE
extensions. These linguistic data are essential not only to name entity extraction but also to the taxonomic classification and to determining the NE frontiers. Our method is also based on the contextualisation and
on the notion of NE class attributes and values. Inspired from X-bar theory and immediate constituents, we built a rule-based NER system composed of five levels of syntactico-semantic combination. We also show how the fine NE annotations in our system output (XML database) is exploited in information retrieval and information extraction.

The main aim of this research paper is to develop a rule based knowledge database for legal expert system
for consumer protection act, a domain within the Indian legal system which is often in demand. A
knowledge database developed here will further help the legal expert system to determine type of case with
respect to the Indian Judicial System. In this paper a rule based knowledge database will be developed to
determine the type of the case. The main aim of the study is to build a prototype which will rule based in
nature. The rule based knowledge database development is the first phase in the development of
comprehensive rule based legal expert system for consumer protection act which will be of great help in
process of solving consumer related cases.

This study was carried out to study the existing paper-based system of receiving information and managing international student’s information at the Office of International Programme (OIP), University of Ibadan and to design an only student information management system that will help the OIP in managing international students’ information efficiently and effectively. Agile Methodology was adopted. System analysis was carried out in order to collect factual data, understand the process involved in the old system, identify problems and recommend feasible suggestions for improving the functioning of the system. In studying the existing system (paper-based), direct and indirect techniques were used in obtaining data for the analysis of the system. The problems of the existing system were identified and Online Student Information Management System was considered a possible solution to the problem of the existing system. The new system provides quick and efficient storage and retrieval o...

The rule-based approach has successfully been used in developing many natural language processing systems. Systems that use rule-based transformations are based on a core of solid linguistic knowledge. The linguistic knowledge acquired for one natural language processing system may be reused to build knowledge required for a similar task in another system. The advantage of the rule-based approach over the corpus-based approach is clear for: 1) less-resourced languages, for which large corpora, possibly parallel or bilingual, with representative structures and entities are neither available nor easily affordable, and 2) for morphologically rich languages, which even with the availability of corpora suffer from data sparseness. These have motivated many researchers to fully or partially follow the rule- based approach in developing their Arabic natural processing tools and systems. In this paper we address our successful efforts that involved rule-based approach for different Arabic n...

Healthcare sector contains large volumes of data. A large volume of health related data is in the form of graphics, images and text. Clinical decision support system is an old area of research but current Electronic Medical Records (EMR) has no decision support system that could create a proper relationship between relevant treatments against their respective diagnoses. This research has a unique idea to incorporate decision support system in EMR. Paper based environment has already been replaced with EMR but still medical providers are facing problems by choosing less relevant procedures, which may not produce the optimal result. Due to this reason claims rejection rate are high and payments are low in numbers. Focus of this research work is to provide a decision support system and to deliver focused knowledge in the form of relationship between diagnoses and their procedures. The system follows three steps to make a decision. Firstly, it checks the past claims for diagnosis and tr...

To know finished priority scale of case in Laboratory of Forensic, in this thesis we developed an expert system with forward and backward chaining methods rule based, used some criteria of case. Priority scale of case is done to sort data and determine the stage of case, which will be used, for examination. This process needs an evaluation in a number on aspect in accordance with the criteria and characteristics of the case. The capabilities of this system accommodates input data cases which user needs, priority scale and stages according to the standard operating procedure in Indonesian National Police especially in Laboratory of Forensic branch in Semarang and gives the visual picture like groove determining of the priority scale also the best solution from the several alternatives used forward and backward chaining methods rule based. The result showed a good and ideal decision for solved case by quickly recommendation and main priority of case with the result of compatibility level was 100%.

Rule learning is one of the most popular areas in machine learning research, because the outcome of learning is to produce a set of rules, which not only provides accurate predictions but also shows a transparent process of mapping inputs to outputs. In general, rule learning approaches can be divided into two main types, namely, 'divide and conquer' and 'separate and conquer'. The former type of rule learning is also known as Top-Down Induction of Decision Trees, which means to learn a set of rules represented in the form of a decision tree. This approach results in the production of a large number of complex rules (usually due to the replicated sub-tree problem), which lowers the computational efficiency in both the training and testing stages, and leads to the overfitting of training data. Due to this problem, researchers have been gradually motivated to develop 'separate and conquer' rule learning approaches, also known as covering approaches, by learning a set of rules on a sequential basis. In particular, a rule is learned and the instances covered by this rule are deleted from the training set, such that the learning of the next rule is based on a smaller training set. In this paper, we propose a new algorithm, GIBRG, which employs Gini-Index to measure the quality of each rule being learned, in the context of 'separate and conquer' rule learning. Our experiments show that the proposed algorithm outperforms both decision tree learning algorithms (C4.5, CART) and 'separate and conquer' approaches (Prism). In addition, it also leads to a smaller number of rules and rule terms, thus being more computationally efficient and less prone to overfitting.

Abstract: Gujarati is a resource poor language with almost no language processing tools being available. In this paper we have shown an implementation of a rule based stemmer of Gujarati. We have shown the creation of rules for stemming and the richness in morphology that Gujarati possesses. We have also evaluated our results by verifying it with a human expert.

Abstract. Institutional change explains the change of institutions considered as rules and expectations that govern human interactions and paths of development in society. This conceptual paper describes, with an historical perspective, the most important definitions of institution and of institutional change and shows some theories that analyze these critical topics in economics to assess similarities and differences. The future challenge of institutional scholars concludes this study. Keywords. Institutions, Institutional theory, Deinstitutionalization. JEL. D02, E02, G20, I20.

Process mining is a technique for extracting process models from event logs. Process mining can be used to discover, monitor and to improve real business processes by extracting knowledge from event logs available in process-aware information systems. This paper is concerned with the problem of grouping events in instances and the preparation of data for the process mining analysis. Often information systems do not store a unique identifier of the case instance, or errors happen in the system during the recording of events in the log files. To be able to analyze the process, it is necessary that events are grouped into case instances. The aim of the presented rule based algorithm is to find events belonging to the same case instance. Performances of the algorithm, for different sizes of log file events and different levels of errors within log files in the real process, have been analyzed.

We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global perspective. First, we formulate a broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables. This novel model class allows us to formalize the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalized Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Notably, we show that our problem definition is equal to mining the top-1 subgroup with an information-theoret...

In this paper we describe how the Predictive Model Markup Language (PMML) standard enhances the JBoss Drools production rule engine with native support for using predictive models in business rules. The historic debate between symbolic and connectionist approaches to rule/model orchestration provides numerous examples of hybrid systems combining "hard" and "soft" computing techniques to achieve different levels of integration. Rules

Prism has been developed as a modular classification rule generator following the separate and conquer approach since 1987 due to the replicated sub-tree problem occurring in Top-Down Induction of Decision Trees (TDIDT). A series of experiments have been done to compare the performance between Prism and TDIDT which proved that Prism may generally provide a similar level of accuracy as TDIDT but with fewer rules and fewer terms per rule. In addition, Prism is generally more tolerant to noise with consistently better accuracy than TDIDT. However, the authors have identified through some experiments that Prism may also give rule sets which tend to underfit training sets in some cases. This paper introduces a new modular classification rule generator, which follows the separate and conquer approach, in order to avoid the problems which arise with Prism. In this paper, the authors review the Prism method and its advantages compared to TDIDT as well as its disadvantages that are overcome by a new method using Information Entropy Based Rule Generation (IEBRG). The authors also set up an experimental study on the performance of the new method in classification accuracy and computational efficiency. The method is also evaluated comparatively with Prism.

The subject of this paper is the control of autonomous vehicles. A hierarchical approach is studied in the context of fuzzy systems and a programming language for the mid to low level control of autonomous vehicles is described. The language, called FDTL (Fuzzy Decision Tree Language), is based on a computational model that combines fuzzy rule based control with the hierarchical nature of decision trees.

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfitting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances . This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suitable structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations: rule generation, rule simplification and rule representation particularly on Big Data. The authors also review some existing methods and techniques used for each of the three operations and highlight the limitations of them as well as introduce some novel methods and techniques developed in their more recent research. The novel methods and techniques are also discussed in comparison to those existing ones reviewed earlier with respects to effective and efficient processing of Big Data.

Healthcare sector contains large volumes of data. A large volume of health related data is in the form of graphics, images and text. Clinical decision support system is an old area of research but current Electronic Medical Records (EMR) has no decision support system that could create a proper relationship between relevant treatments against their respective diagnoses. This research has a unique idea to incorporate decision support system in EMR. Paper based environment has already been replaced with EMR but still medical providers are facing problems by choosing less relevant procedures, which may not produce the optimal result. Due to this reason claims rejection rate are high and payments are low in numbers. Focus of this research work is to provide a decision support system and to deliver focused knowledge in the form of relationship between diagnoses and their procedures. The system follows three steps to make a decision. Firstly, it checks the past claims for diagnosis and tr...

The work presents design and development of a system to automatically evolve rules through genetic-fuzzy approach. The work highlights the advantages of genetic and fuzzy hybridization and proposes a framework to evolve rules automatically. The objective of the framework is to reduce the development effort and guide the development procedure in user friendly fashion. The architecture presents a novel approach which integrates fuzzy logic with system design as well as back end of the framework is used to evolve the fuzzy rules using genetic algorithm approach. To experiment the working of the proposed framework, a system to measure multiple intelligence is selected. The paper describes the detail methodology including encoding strategy of rules, suitable genetic operators and fitness function to evolve fuzzy rules for the selected system. The initial population of rules, evolved generations and output results are also described. The paper concludes with the scope and applications of ...