Small Set Of Rules Research Articles

A new approach to classification based on association rule mining

Classification is one of the key issues in the fields of decision sciences and knowledge discovery. This paper presents a new approach for constructing a classifier, based on an extended association rule mining technique in the context of classification. The characteristic of this approach is threefold: first, applying the information gain measure to the generation of candidate itemsets; second, integrating the process of frequent itemsets generation with the process of rule generation; third, incorporating strategies for avoiding rule redundancy and conflicts into the mining process. The corresponding mining algorithm proposed, namely GARC (Gain based Association Rule Classification), produces a classifier with satisfactory classification accuracy, compared with other classifiers (e.g., C4.5, CBA, SVM, NN). Moreover, in terms of association rule based classification, GARC could filter out many candidate itemsets in the generation process, resulting in a much smaller set of rules than that of CBA.

A compositional approach to [formula omitted] verification

The paper presents a compositional approach to the verification of CTL * properties over reactive systems. Both symbolic model-checking ( SMC) and deductive verification are considered. Both methods are based on two decomposition principles. A general state formula is decomposed into basic state formulas which are CTL * formulas with no embedded path quantifiers. To deal with arbitrary basic state formulas, we introduce another reduction principle which replaces each basic path formula, i.e., path formulas whose principal operator is temporal and which contain no embedded temporal operators or path quantifiers, by a newly introduced boolean variable which is added to the system. Thus, both the algorithmic and the deductive methods are based on two statification transformations which successively replace temporal formulas by assertions which contain no path quantifiers or temporal operators. Performing these decompositions repeatedly, we remain with basic assertional formulas, i.e., formulas of the form E f p and A f p for some assertion p . In the model-checking method we present a single symbolic algorithm to verify both universal and existential basic assertional properties. In the deductive method we present a small set of proof rules and show that this set is sound and relatively complete for verifying universal and existential basic assertional properties over reactive systems. Together with two proof rules for the decompositions, we obtain a sound and relatively complete proof system for arbitrary CTL * properties. Interestingly, the deductive approach for CTL * presented here, offers a viable new approach to the deductive verification of arbitrary LTL formulas.

PORSEL: an expert system for assisting in investment analysis and valuation

During the last few years, we have developed an expert system, called PORSEL (PORtfolio SELection system), which uses a small set of rules to select stocks. PORSEL consists of three components: the Information Center, the Fuzzy Stock Selector, and the Portfolio Constructor. The purpose of the information center is to provide representation of several technical indicators such as candlestick charts, moving average of closing prices, and price trends. The fuzzy stock selector evaluates the listed stocks and then assigns a composite score for each stock. The portfolio constructor generates the optimal portfolios for the selected stocks. The PORSEL also includes a user-friendly interface for adding and deleting rules during the run time. The results of simulation shows that PORSEL outperformed the market almost every year during the testing period.

Evaluation of energy conservation programs in residential sector using fuzzy logic methodology

In this field investigation, a new approach for evaluating energy conservation and awareness programs within residential consumers is presented and discussed. It is based on expert computer knowledge-based systems and fuzzy set analyses. Computer-programmed surveys were carried out on a random sample of energy users in Jordan, such as students, housewives and educated and uneducated citizens, before and after they received a simple energy educational material. The fuzzy set logic technique enabled to condense large data into a small set of variable rules. Relative weights were given for each response and a cumulative grade was estimated. This in turn made the employed expert system effective in evaluating energy conservation programs. The follow-up survey revealed that a reasonable rise, of about 15%, of energy related knowledge was achieved, as compared to the initial survey. Fuzzy set analyses suggest that some fluctuations in the relative rise of public awareness programs may be attributed to insufficient knowledge. Thus, through public awareness and demonstration programs as well as incentives, such as financial assistance and technical aid to encourage energy end-users, in all sectors, to employ the latest technologies and more energy-efficient equipment, final energy demand can be reduced or controlled. Hence, energy efficiency should be incorporated as a corner stone of the national energy policy.

Interactive Cutaway Illustrations

Abstract In this paper we discuss different approaches to generate cutaway illustrations. The purpose of such a drawingis to allow the viewer to have a look into an otherwise solid opaque object. Traditional methods to draw thesekinds of illustrations are evaluated to extract a small and effective set of rules for a computer‐based renderingof cutaway illustrations. We show that our approaches are not limited to a specific rendering style but can besuccessfully combined with a great variety of well‐known artistic or technical illustration techniques. All methodsof this paper make use of modern graphics hardware functionality to achieve interactive frame rates.

Composite primal/dual [formula omitted]-subdivision schemes

We present new families of primal and dual subdivision schemes for triangle meshes and 3 -refinement. The proposed schemes use two simple local rules which cycle between primal and dual meshes a number of times. The resulting surfaces become very smooth at regular vertices if the number of cycles is ⩾2. The C 1-property is violated only at low-valence irregular vertices, and can be restored by slight modifications of the local rules used. As a generalization, we introduce a wide class of composite subdivision schemes suitable for arbitrary topologies and refinement rules. A composite scheme is defined by a simple upsampling from the coarse to a refined topology, embedded into a cascade of geometric averaging operators acting on coarse and/or refined topologies. We propose a small set of such averaging rules (and some of their parametric extensions) which allow for the switching between control nets associated with the same or different topologic elements (vertices, edges, faces), and show a number of examples, based on triangles, that the resulting class of composite subdivision schemes contains new and old, primal and dual schemes for 3 -refinement as well as for quadrisection. As a common observation from the examples considered, we found that irregular vertex treatment is necessary only at vertices of low valence, and can easily be implemented by using generic modifications of some elementary averaging rules.

Expert computer knowledge-based methodology using fuzzy sets for evaluating water conservation programs in Jordan

This paper aims to devise a new methodology for evaluating water conservation programs based on expert computer knowledge-based systems and fuzzy set analyses. Computer-programmed surveys were carried out on a random sample of water users in Amman City, Jordan, before and after they received water educational material. Relative weights were given for each response and a cumulative grade was estimated. The follow-up surveys reveal that a reasonable rise of water-related knowledge is achieved after respondents are exposed to water educational material. Fuzzy set analyses reveal that some fluctuations in the relative rise of public awareness programs may be attributed to insufficient knowledge. The fuzzy set logic technique enabled us to condense a great many data into a small set of variable rules. This in turn made our expert system effective in evaluating water conservation programs.

Extracting regression rules from neural networks

This paper proposes a new framework and method for extracting regression rules from neural networks trained with multivariate data containing both nominal and numeric variables. Each regression rule is expressed as a pair of a logical formula on the conditional part over nominal variables and a polynomial equation on the action part over numeric variables. The proposed extraction method first generates one such regression rule for each training sample, then utilizes the k-means algorithm to generate a much smaller set of rules having more general conditions, where the number of distinct polynomial equations is determined through cross-validation. Finally, this method invokes decision-tree induction to form logical formulae of nominal conditions as conditional parts of final regression rules. Experiments using four data sets show that our method works well in extracting quite accurate and interesting regression rules.

Transparency in Interactive Technical Illustrations

This paper describes how technical illustrations containing opaque and non-opaque objects can be automatically generated. Traditional methods to show transparency in manual drawings are evaluated to extract a small and effective set of rules for computer-based rendering of technical illustrations, leading to a novel view-dependent transparency model. We propose a hardware-accelerated depth sorting algorithm in image-space which specifically meets the requirements of our transparency model. In this way, real-time rendering of semi-transparent technical illustrations is achieved. Finally, it is described how our approach can be combined with other methods in the field of non-photorealistic rendering in order to enhance the visual perception of technical illustrations. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Color, shading, shadowing and texture

Simple association rules (SAR) and the SAR-based rule discovery

Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a problem of concern, as conventional mining algorithms often produce too many rules for decision makers to digest. Instead, this paper concentrates on a smaller set of rules, namely, a set of simple association rules each with its consequent containing only a single attribute. Such a rule set can be used to derive all other association rules, meaning that the original rule set based on conventional algorithms can be ‘recovered’ from the simple rules without any information loss. The number of simple rules is much less than the number of all rules. Moreover, corresponding algorithms are developed such that certain forms of rules (e.g. ‘P⇒?’ or ‘?⇒Q’) can be generated in a more efficient manner based on simple rules.

F AN: Finding Accurate iNductions

In this paper we present a machine-learning algorithm that computes a small set of accurate and interpretable rules. The decisions of these rules can be straight-forwardly explained as the conclusions drawn by a case-based reasoner. Our system is named F AN, an acronym for f inding a ccurate i n ductions. It starts from a collection of training examples and produces propositional rules able to classify unseen cases following a minimum-distance criterion in their evaluation procedure. In this way, we combine the advantages of instance-based algorithms and the conciseness of rule (or decision-tree) inducers. The algorithm followed by F AN can be seen as the result of successive steps of pruning heuristics. The main tool employed is that of the impurity level, a measure of the classification quality of a rule, inspired by a similar measure used in IB3. Finally, a number of experiments were conducted with standard benchmark datasets of the UCI repository to test the performance of our system, successfully comparing F AN with a wide collection of machine-learning algorithms.

Energetics of muscle contraction: the whole is less than the sum of its parts

Understanding muscle energetics is a problem in optimizing supply of ATP to the demands of ATPases. The complexity of reactions and their fluxes to achieve this balance is greatly reduced by recognizing constraints imposed by the integration of common metabolites at fixed stoichiometry among modular units. ATPase is driven externally. Oxidative phosphorylation and glycogenolysis are the suppliers. We focus on their regulation which involves different controls, but reduces to two principles that enable facile experimental analysis of the supply and demand fluxes. The ratio of concentration of phospho-creatine (PCr) to ATP, not their individual values, sets the range of achievable concentrations of ADP in resting and active muscle (at fixed pH) in different cell types. This principle defines the fraction of available flux of oxidative phosphorylation utilized (at fixed enzyme activities). Then the kinetics of PCr recovery defines the kinetics of oxygen supply and substrate utilization. The second principle is the constancy of PCr and H+ (lactate) production by glycogenolysis due to the coupling of ATPase and glycolysis. This principle enables glycogenolytic flux to be measured from intracellular proton loads. Further simplification occurs because the magnitude of the interacting fluxes and metabolite concentrations are specified within narrow limits when both the resting and active fluxes are quantified. Thus there is a small set of rules for assessing and understanding the thermodynamics and kinetics of muscle energetics.

Cellular automata microsimulation for modeling bi-directional pedestrian walkways

Pedestrian flow is inherently complex, more so than vehicular flow, and development of microscopic models of pedestrian flow has been a daunting task for researchers. This paper presents the use of Cellular automata (CA) microsimulation for modeling bi-directional pedestrian walkways. It is shown that a small rule set is capable of effectively capturing the behaviors of pedestrians at the micro-level while attaining realistic macro-level activity. The model provides for simulating three modes of bi-directional pedestrian flow: (a) flows in directionally separated lanes, (b) interspersed flow, and (c) dynamic multi-lane (DML) flow. The emergent behavior that arises from the model, termed CA-Ped, is consistent with well-established fundamental properties.

Scale-invariant analysis of wear particle morphology—a preliminary study

The importance of wear particle characterization is continuously growing, as the need for prediction and monitoring of wear increases. Accurate analysis of wear particles can, however, be limited by problems associated with particle characterization, especially of the wear particles' surface morphology. Since the shape and surface topography of wear particles often exhibit a fractal nature, fractal (scale-invariant) methods are, therefore, used in their characterization. However, the methods used to date ignore the fact that all fractal objects can be described by a small set of mathematical rules; although finding those rules which describe a particular fractal image is a difficult problem. No general solution exists to date and this paper attempts to redress this problem. A new analysis method, ‘scale-invariant analysis’, which is based on a partitioned iterated function system (PIFS), is proposed for the characterization of wear particle morphology. PIFS is a collection of contractive affine transformations. Each affine transformation transforms one part of a wear particle image onto another part of the same image. PIFSs were constructed for both computer generated and SEM images of wear particles. Results obtained in this study clearly demonstrate that the morphology of wear particles can effectively be characterized using the PIFS method.

Nonlinear Rule-based Convolution for Refocusing

We seek an efficient enhancement process that sharpens an image and yet smooths areas of low differentials and removes low-level impulse noise. The goal is to perform these antithetical processes to bring a fairly good image into better focus that is not sufficiently sharp. Upon adopting the perspective of a constructor beetle that explores each pixel of an image and examines its neighboring pixels, we find a small set of operational rules. If the neighbors have similar height (gray level) to the center pixel, then the beetle digs or fills a small amount to further level the neighborhood (smoothing). On the other hand, if the neighbors tend to be very different on the average so they appear as a barrier of holes or poles, then the beetle digs or fills to construct a barrier. The beetle also levels a single deep hole or high pole at the neighborhood center, which smooths impulse noise.

A SIMPLE AND ROBUST THINNING ALGORITHM

In this paper a new thinning algorithm is presented. It is simple yet robust, and can maintain the same advantages of other key skeletonization algorithms. In this new algorithm, only a very small set of rules of criteria for deleting pixels is used. It is faster and easier to implement. Its advantages and limitations are discussed and compared with others. Several examples are illustrated.

Self-organizing nest construction in ants: individual worker behaviour and the nest's dynamics

We examine nest construction in the antLeptothorax tuberointerruptusat two levels: (1) the building behaviour of individual workers and (2) the collective properties (temporal and spatial) of the structures they create. We also explore, for the first time explicitly, the linkage between these two levels.Leptothorax tuberointerruptusnests occur in flat cavities which provide the roof and the floor of their dwelling places. Hence, they construct only a peripheral encircling wall, breached by one or more entrance passageways. The wall is constructed brick by brick. This facilitates experimental estimation of the probabilities of individual workers picking up and depositing building material in response to different stimuli. We incorporate both the qualitative and quantitative behavioural rules that works employ during building into a mathematical model. This model confirms that a surprisingly small and simple set of behavioural rules are not only sufficient for wall construction but also for the formation of one or more nest entrances. In addition, this model predicts that the nests of these ants are likely to exhibit interesting dynamics, in which, for example, the tendency to build a new larger nest may lag behind growth of the population that the nest has to house. We present experimental evidence that suggests that this prediction is valid.

Predicate transformers for reasoning about concurrent computation

In this paper we propose a calculus for reasoning about concurrent programs inspired by the wp calculus for reasoning about sequential programs. The calculus uses a small set of familiar rules for dealing with safety, progress and parallel composition. A contribution of this paper is to demonstrate how predicate calculus in general, and predicate transformers in particular, can be used to reason about concurrent programs in which fairness plays a critical role.

A hybrid approach for real-time sequencing and scheduling

Abstract An approach for real-time sequencing and scheduling is described which integrates artificial neural networks, real-time simulation, genetic algorithms, and a trace-driven knowledge acquisition technique. This approach will be used to solve both single machine sequencing and multi-machine scheduling problems. Single-performance, artificial neural nets are used to quickly generate a small set of candidate sequencing or scheduling rules from some larger set of heuristics. A more detailed evaluation of these candidates is carried out using realtime simulation. This evaluation is necessary to generate a ranking that specifics how each rule performs against all of the performance measures. A further reduction of these candidates can be achieved from this ranking. Genetic algorithms are applied to this remaining set of rules to generate a single 'best' schedule. To capture the knowledge contained in that schedule, a trace-driven knowledge acquisition technique is used. Then, the derived rule is added to...

Transforming high-level data-parallel programs into vector operations

Efficient parallel execution of a high-level data-parallel language based on nested sequences, higher order functions and generalized iterators can be realized in the vector model using a suitable representation of nested sequences and a small set of transformational rules to distribute iterators through the constructs of the language.