Optimal Rule Set Research Articles

Anchor me: the benefits and challenges of fiscal responsibility

This paper discusses the benefits and challenges of implementing a rule‐based fiscal responsibility framework, using the Philippines as a case study. It estimates structural measures of the fiscal stance over the period 1980–2016 and applies a stochastic simulation model to determine the optimal set of fiscal rules. The empirical analysis indicates that discretionary fiscal policy has been procyclical, and the degree of procyclicality has increased in recent years. While the national government's nonbinding ceiling on the overall budget deficit is helpful, it does not constitute an appropriate operational target to guide fiscal policy over the economic cycle and necessarily ensure that the fiscal stance meets the government's intertemporal budget constraint. To this end, I use stochastic simulations and show the macroeconomic benefits of a well‐designed fiscal responsibility law that enshrines explicit fiscal rules designed for countercyclical policy and long‐term debt sustainability and an independent fiscal council that improves accountability and transparency.

Anchor Me: The Benefits and Challenges of Fiscal Responsibility

This paper discusses the benefits and challenges of implementing a rule-based fiscal responsibility framework, using the Philippines as a case study. It estimates structural measures of the fiscal stance over the period 1980–2016 and applies a stochastic simulation model to determine the optimal set of fiscal rules. The empirical analysis indicates that discretionary fiscal policy has been procyclical, and the degree of procyclicality has increased in recent years. While the national government’s non-binding ceiling on the overall budget deficit is helpful, it does not constitute an appropriate operational target to guide fiscal policy over the economic cycle and necessarily ensure that the fiscal stance meets the government’s intertemporal budget constraint. To this end, using stochastic simulations, this paper makes the case for a well-designed fiscal responsibility law that enshrines explicit fiscal rules designed for countercyclical policy and long-term debt sustainability, and an independent fiscal council to improve accountability and transparency.

Back to the Future: Fiscal Rules for Regaining Sustainability

This paper assesses the cyclicality and sustainability of fiscal policy in Belize and applies a stochastic simulation model to determine the optimal set of fiscal rules. The empirical analysis shows that fiscal policy in Belize has been significantly procyclical and unsustainable much of the period since 1976. While the government's recent commitment to maintain a primary surplus of at least 2 percent of GDP until 2021 is supporting debt reduction, stochastic simulations indicate that further improvement in the primary balance is necessary to reliably bring the debt-to-GDP ratio to a sustainable path. Given Belize's history of large economic shocks, this paper proposes explicit fiscal rules designed for countercyclical policy and debt sustainability. It recommends integrating such rules into a well-designed fiscal responsibility law and establishing an independent fiscal council to improve accountability and transparency.

Anchor Me: The Benefits and Challenges of Fiscal Responsibility

This paper discusses the benefits and challenges of implementing a rule-based fiscal responsibility framework, using the Philippines as a case study. It estimates structural measures of the fiscal stance over the period 1980–2016 and applies a stochastic simulation model to determine the optimal set of fiscal rules. The empirical analysis indicates that discretionary fiscal policy has been procyclical, and the degree of procyclicality has increased in recent years. While the national government’s non-binding ceiling on the overall budget deficit is helpful, it does not constitute an appropriate operational target to guide fiscal policy over the economic cycle and necessarily ensure that the fiscal stance meets the government’s intertemporal budget constraint. To this end, using stochastic simulations, this paper makes the case for a well-designed fiscal responsibility law that enshrines explicit fiscal rules designed for countercyclical policy and long-term debt sustainability, and an independent fiscal council to improve accountability and transparency.

Construction of the optimal set of quadrature rules in the sense of Borges

Construction of the optimal set of quadrature rules in the sense of Borges

"Fuzzy Qualitative Reasoning Model for Astrocytoma Brain Tumor Grade Diagnosis"

Background: Magnetic Resonance Imaging (MRI) is the most prominently used image acquisition method for brain tumor diagnosis, treatment and research. Objective: In this paper, a fuzzy qualitative reasoning model for diagnosing the grade of Astrocytoma brain tumor using various subtypes of MR images (T1, T1c+, T2, Flair) is explained with its implementation details. Methods: The fuzzy model is implemented in 5 stages namely preprocessing, segmentation, feature extraction, feature selection and building a Fuzzy Inference System (FIS) for diagnosis. In preprocessing, anisotropic filtering is used to remove noise and artifacts whereas the edge information and smoothness are retained. Then the tumor region is segmented by applying active contour method. From the segmented tumor region, textural and shape features are extracted and stored along with the clinical parameters like age, gender and mass effect of the patient for feature selection. The features are analyzed in different dimensions like image, patient, patient with subtype, to determine the sensitive feature subset and its range that discriminates the grade of the tumor. Based on this outcome a Mamdani based fuzzy qualitative reasoning model is built with optimal rule set for tumor grade diagnosis. Findings: The constructed fuzzy model is validated using real data set of MR images and clinical report of patients. The grade of tumor identified is same as that specified in the patient\'s report and hence the model provides better accuracy. Novelty: The novelty of this research work are: subtypes of MR images with analysis in different dimensions, identification of optimal rule set (minimum number of rules without ambiguity), recognition of irregular shape tumor, suitable model for any knowledge based diagnosis. Keywords: Active Contour Method, Anisotropic Filtering, Astrocytoma Brain Tumor, Fuzzy Qualitative Reasoning Model, Magnetic Resonance Images, Optimal Rule Set, Textural and Shape Features

Usage of co-event pattern mining with optimal fuzzy rule-based classifier for effective web page retrieval

With the coming of the World Wide Web and the rise of web-based business applications and informal organizations, associations over the web create a lot of information on a daily basis. It is becoming more complex and critical task to retrieve exact information from web expected by its users. In the recent times, the Web has extended its noteworthiness to the point of transforming into the point of convergence of our propelled lives. The search engine as an apparatus to explore the web must get the coveted outcomes for any given query. The greater part of the search engines can't totally fulfill user’s necessities and the outcomes are regularly inaccurate and irrelevant. knowledge of ontology and history is not much personalization in the existing techniques. To conquer these issues, data mining systems must be connected to the web and one advanced powerful concept is web-page recommendation which is becoming more powerful now a day. In this paper, the design of a fuzzy logic classifier algorithm is defined as a search problem in the solution space where every node represents a rule set, membership function, and the particular framework behaviour. Therefore, the hybrid optimization algorithm is applied to search for an optimal location of this solution space which hopefully represents the near optimal rule set and membership function. In this article, we reviewed various techniques proposed by different researchers for web page personalization and proposed a novel approach for finding optimal solutions to search the relevant information..

Symbolic interpretation of artificial neural networks based on multiobjective genetic algorithms and association rules mining

Rule extraction from neural networks is an important task. In practice, decision makers often settle for using less accurate, but comprehensible models, typically decision trees where the solutions are given in graphical form easily interpretable. The black-box rule extraction techniques, operating directly on the input-output relationship, are clearly superior to the restricted open-box methods, normally tailored for a specific architecture. This is especially important since most data miners today will use some kind of ensemble (instead of a single model) to maximize accuracy. Consequently, the ability to extract rules from any opaque model is a key demand for rule extraction techniques. This paper proposes a new multiobjective genetic method to extract knowledge from trained artificial neural network by using the association rules technique. The main aim of this hybridization is to extract the optimal rules from the neural network for further classification. The algorithm consists of two stages: the rule filtering phase which eliminates misleading rules by taking into account the support, the confidence and the lift measures, then, rule set optimization phase which finds the set of optimal rule sets by considering fidelity, coverage and complexity measures. The algorithm is evaluated on 05 UCI datasets. The experimental results show that the proposal provides interesting rules. Accuracy and comprehensibility are clearly improved, and subsequently, it can become a challengeable and trustful research field in the area of neural network rule extraction.

Generating Classification Rules from Training Samples

In this paper, we describe an algorithm to extract classification rules from training samples using fuzzy membership functions. The algorithm includes steps for generating classification rules, eliminating duplicate and conflicting rules, and ranking extracted rules. We have developed software to implement the algorithm using MATLAB scripts. As an illustration, we have used the algorithm to classify pixels in two multispectral images representing areas in New Orleans and Alaska. For each scene, we randomly selected 10 per cent of the samples from our training set data for generating an optimized rule set and used the remaining 90 per cent of samples to validate the extracted rules. To validate extracted rules, we built a fuzzy inference system (FIS) using the extracted rules as a rule base and classified samples from the training set data. The results in terms of confusion matrices are presented in the paper.

Data‐driven car‐following model based on rough set theory

The car-following model is an important micro-traffic model for simulating car-following behaviour in traffic engineering and research studies. Conventional car-following models are always presented using mathematical equations reflecting ideal traffic conditions. In the big data era, data-driven models become a popular trend. In this study, a data-driven car-following model based on the rough set theory is proposed to consider information hidden in a field data set. On the basis of field data obtained from measurement devices such as the next generation simulation (NGSIM) trajectory data set, and using the methods of the rough set theory, an optimal decision rule set is established. Redundant attributes and redundant attribute values are removed for simplifying the car-following behaviour decision problem. Attribute significance and weights are computed for selecting matching rules. A car-following behaviour decision algorithm is designed to choose appropriate rules to determine the follower's velocity according to current observations. Simulations illustrate that the proposed data-driven car-following model can simulate the micro-traffic behaviour of followers well.

Construction of the optimal set of two or three quadrature rules in the sense of Borges

In this paper, we investigate a numerical method for the construction of an optimal set of quadrature rules in the sense of Borges (Numer. Math. 67, 271–288, 1994) for two or three definite integrals with the same integrand and interval of integration, but with different weight functions, related to an arbitrary multi-index. The presented method is illustrated by numerical examples.

Artificial Immune Classifier Based on ELLipsoidal Regions (AICELL) †

Pattern classification is a central problem in machine learning, with a wide array of applications, and rule-based classifiers are one of the most prominent approaches. Among these classifiers, Incremental Rule Learning algorithms combine the advantages of classic Pittsburg and Michigan approaches, while, on the other hand, classifiers using fuzzy membership functions often result in systems with fewer rules and better generalization ability. To discover an optimal set of rules, learning classifier systems have always relied on bio-inspired models, mainly genetic algorithms. In this paper we propose a classification algorithm based on an efficient bio-inspired approach, Artificial Immune Networks. The proposed algorithm encodes the patterns as antigens, and evolves a set of antibodies, representing fuzzy classification rules of ellipsoidal surface, to cover the problem space. The innate immune mechanisms of affinity maturation and diversity preservation are modified and adapted to the classification context, resulting in a classifier that combines the advantages of both incremental rule learning and fuzzy classifier systems. The algorithm is compared to a number of state-of-the-art rule-based classifiers, as well as Support Vector Machines (SVM), producing very satisfying results, particularly in problems with large number of attributes and classes.

Quantified moving average strategy of crude oil futures market based on fuzzy logic rules and genetic algorithms

The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.

SM-RuleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification

Diabetes is a major health challenge around the world. Existing rule-based classification systems have been widely used for diabetes diagnosis, even though they must overcome the challenge of producing a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity values. To resolve this drawback, in this paper, a Spider Monkey Optimization-based rule miner (SM-RuleMiner) has been proposed for diabetes classification. A novel fitness function has also been incorporated into SM-RuleMiner to generate a comprehensive optimal ruleset while balancing accuracy, sensitivity and specificity. The proposed rule-miner is compared against three rule-based algorithms, namely ID3, C4.5 and CART, along with several meta-heuristic-based rule mining algorithms, on the Pima Indians Diabetes dataset using 10-fold cross validation. It has been observed that the proposed rule miner outperforms several well-known algorithms in terms of average classification accuracy and average sensitivity. Moreover, the proposed rule miner outperformed the other algorithms in terms of mean rule length and mean ruleset size.

Quality contests

In noisy contests where only the winner's entry will eventually be implemented, the suitable objective is to maximize the expected quality of the entry of the winner. We compare the optimal set of rules in contests under such an objective to the one under maximization of the sum of contestants’ efforts, which is commonly assumed in the literature, and find that it may be beneficial to exclude weak contestants, unlevel the playing field, and weaken the underdog.

Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications

In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of $\mathcal{O}(N^2)$ in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.

Parallel construction of classification trees on a GPU

SummaryAlgorithms for constructing tree‐based classifiers are aimed at building an optimal set of rules implicitly described by some dataset of training samples. As the number of samples and/or attributes in the dataset increases, the required construction time becomes the limiting factor for interactive or even functional use. The problem is emphasized if tree derivation is part of an iterative optimization method, such as boosting. Attempts to parallelize the construction of classification trees have therefore been presented in the past. This paper describes a parallel method for binary classification tree construction implemented on a graphics processing unit (GPU) using compute unified device architecture (CUDA). By employing node‐level, attribute‐level, and split‐level parallelism, the task parallel and data parallel sections of tree induction are mapped to the architecture of a modern GPU. The GPU‐based solution is compared with the sequential and multi‐threaded CPU versions on public access datasets, and it is shown that an order of magnitude acceleration can be achieved on this data‐intensive task using inexpensive commodity hardware. The influence of dataset characteristics on the efficiency of parallel implementation is also analyzed. Copyright © 2015 John Wiley & Sons, Ltd.

An enhanced self-adaptive differential evolution based on simulated annealing for rule extraction and its application in recognizing oil reservoir

This study proposes an enhanced self-adaptive differential evolution based on simulated annealing for rule extraction (ESADE-Rule). ESADE-Rule adopts IF-THEN as the rule's form, AND as the connection word for the rule's antecedent, class label as the rule's consequence. Rules are encoded as individuals in population of ESADE, and each individual represents a rule set consisting of three parts: the rule's parameters (including the controls of the rule, antecedents and class labels), the control parameters (including scaling factors and crossover rates), and the fitness value. Mutation and crossover operations of ESADE are further carried out on the population. Then, selection operation is conducted by comparing the fitness values, through which the best individual would be selected out and be decoded to obtain the optimal rule set. Besides, ten benchmark datasets and three logging datasets are adopted to test ESADE-Rule's performance. The three logging datasets contain oilsk81, oilsk83 and oilsk84 that come from Jianghan oilfield for testing recognition accuracy rate of reservoir. ESADE-Rule is compared with four rule extraction methods from the perspectives of recognition accuracy rate, rules' number, antecedents' number and samples' number that are not covered by the rule set. The results prove that ESADE-Rule performs better at recognition accuracy rate and interpretability. With oilsk81 as training data set, oilsk83 and oilsk84 as testing data set, the testing results of recognition accuracy rate of oil reservoirs illustrate that compared with other four rule extraction methods, ESADE-Rule can obtain more general rules set when the attributes of datasets are similar.

A Swarm Optimization approach for clinical knowledge mining

Background and objectivesRule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. MethodsWSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. ResultsExperiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. ConclusionWSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy.

Domain Independent Framework for Automatic Text Summarization

Abstract Due to the exponential growth of documents on internet, users want all the relevant data at one place without any hassle. This led to the growth of Automatic Text Summarization. For this purpose a number of methods have been proposed by researchers but no method is able to work on all domains of text documents. Some methods which work for News domain may fail in Medical domain to give efficient results. In this paper we proposed a domain independent framework for Automatic Text Summarization. The Process first categorises the source text and then it applies the respective category's optimal set of rules or weights or method. The major advantage of framework is that it can be applicable for both extractive and abstractive text summarization.