Top PDF Using Evolving Fuzzy Classifiers to Classify Consumers with Different Model Architectures

Using Evolving Fuzzy Classifiers to Classify Consumers with Different Model Architectures

Using Evolving Fuzzy Classifiers to Classify Consumers with Different Model Architectures

This study introduces two alternative methods for evolving fuzzy classifiers (eClass and FLEXFIS-CLass) in order to classify consumers into different categories for directing marketing purposes. We describe in detail the learning mechanisms of these classifiers and different types of model architectures including single model architectures (SM) and multi-model architectures (MM). Note that single-model architectures have different consequents: singletons corresponding to class labels, linear consequents regressing over the features and eClass MIMO which is applicable in multi-class classification. Furthermore, we place emphasis on classification accuracy and effectiveness of these approaches and compare the proposed classifiers with well-established ones, such as CART and k-NN, and also popular SVM method. The result indicates that they compare favorably with others in term of precision. With these different model architectures, managers can use the introduced approaches to classify consumers to their categories and determine the most profitable decisions.
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Architectures of evolving fuzzy rule based classifiers

Architectures of evolving fuzzy rule based classifiers

Abstract— In this paper the recently introduced evolving fuzzy classifier method called eClass is studied in respect to its architecture and evolution of the fuzzy rule-base. The proposed classifier has an open/evolving structure and can start ‘from scratch’, learning and adapting to the new data samples. Alternatively, if an initial fuzzy rule-based classifier, generated beforehand in off-line mode or provided by the operator, exists then eClass can evolve this initial classifier in on-line mode. In other words, the fuzzy rule base will evolve incorporating new rules, modifying and/or, possibly, removing some of the previously existing ones. Additionally, the parameters of both, the antecedent and the consequent parts are adapted. Note that eClass can start with an empty rule-base, which is a unique feature of this approach. The proposed approach is free from user-specified parameters and the mechanism of forming new rules is very robust. In this paper, four different modelling architectures are described and compared. The architectures are based on i) unsupervised cluster partitions, eClassC; ii) Sugeno fuzzy models with singleton consequents, eClassA; iii) Takagi-Sugeno fuzzy models with linear consequent functions, eClassB; and iv) a multi-model classification architecture, where separate TS regression models are combined to form an overall classification output of the system, eClassM. A thorough comparison of the results when applying each of these architectures and the results using previously existing classifiers has been made using an online interactive self-adaptive image classification framework.
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Evolving single  and multi model fuzzy classifiers with FLEXFIS class

Evolving single and multi model fuzzy classifiers with FLEXFIS class

In this paper a different approach is proposed, FLEXFIS- Class, which uses both, multi-model architecture based on the idea of regression by an indicator matrix (FLEXFIS- Class MM) and single-model architecture (’classical’ fuzzy classification models) (FLEXFIS-Class SM). Both variants of FLEXFIS-Class are basically deduced from FLEXFIS [9], which serves as an evolving method for building up fuzzy regression models fully automatically and adaptively with new incoming data points (from measurement signals, data streams etc.). For both model architectures, the evolving mechanism for the rule antecedent parts takes place in the clusters space by using an evolving version of vector quantization [12] (the original VQ in [13]), including cluster evolution, an alternative winner selection strategy and updates of cluster surfaces synchronously to their centres. In the single-model case the evolution of the consequents (=single class labels) and rule weights is based on a plurality choice and relative frequency of classes in the different clusters (rules), which both can be updated sample-wise (Section II). In the multi- model case one Takagi-Sugeno fuzzy regression model [14] is trained for each separate class and their continuous outputs are aggregated to an overall classification statements (Section III). The incremental training of the consequents in the TS models (hyper-planes) is carried out by exploiting recursive weighted least squares [15] (also applied for consequent adaptation in [16]). Improving the fuzzy classifiers towards approximation accuracy (dealing with drifts in online data streams [17]) is described in Section IV. The paper is concluded in Section V with an evaluation of the proposed approaches within an online adaptive image classification framework and based on a pen-digit recognition data set from the UCI-repository. This evaluation includes a comparison of the impact of the two model architectures on prediction accuracy and model
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Different initial conditions in fuzzy Tumor model

Different initial conditions in fuzzy Tumor model

In this paper, a model of third degree consists of tu- mor cells, immune system cells and normal cells are used and method of solving fuzzy differential inclusion are expressed and all areas of response three-dimen- sional figures using the above method are determined. The purpose of this article is entering uncertainty into tumor model by considering fuzzy model parameters. Fuzzy parameters change crisp system into fuzzy system. Areas of uncertainty for the number of tumor cells in terms of membership function and time are determined. Also in this model, different initial conditions are evalu- ated by their results of simulation. The main purpose of this article is creation a new view of cancer noted that simulation could be stomata for complex phenomena to determine the uncertainty area. This paper is organized as follows. In Section 2, the model from Depillis in 2003 and its mathematical equations are presented. In Sections 3 and 4 the fuzzy model-based and the method reached the fuzzy surfaces is described, and then simulation re- sults of the different initial conditions are evaluated. Section 5 presents some conclusions.
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Modeling and Verifying Dynamic Evolving Service-Oriented Architectures

Modeling and Verifying Dynamic Evolving Service-Oriented Architectures

In this report we want to introduce the concepts of SoaML together with our running example. We have chosen a service-oriented supply-chain network as application example. In a supply-chain network arbitrary factories can participate as long as they fulfill some minimal requirements. A supply-chain network can be recursively built from any factory delivering a product. The supply-chain network of a factory C is the union of the supply-chain networks of all factories, C buys products from, with C being the new root element. Factories that sell raw materials have a supply-chain network that contains only themselves. In domains such as the automotive or avionic industry these supply-chain network become easily very large. Also the business relationships among factories may change often, depending on several constraints such as required product quality, production costs, delivery deadlines to only name a few. However, despite all this it has to be ensured that each factory within the supply-chain network delivers the requested product. The contract negotiation between two factories is established through a service contract. The exact form of the contract is not specified as this might be dependent on the different domains and products.
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USING UML TO MODEL DISTRIBUTED SYSTEM ARCHITECTURES

USING UML TO MODEL DISTRIBUTED SYSTEM ARCHITECTURES

to different configuration stages. A systematic approach for distributed system configuration is presented in [12]. In order to ensure distributed system performance, their configuration dependencies must by identified and explored. Since the underlying network topology affects application configuration, the relationship between resource allocation policy and network architecture should be easily explored, thus models used for the representation of distributed system architectures within each stage should be exchangeable. Configuration stages are supported by automated or semi-automated tools [3, 5, 10, 11, 18]. In order to provide exchangeable models, the modeling framework adopted within each stage should be realizable in various software tools. A common model representing distributed systems in all configuration stages will facilitate model exchange and ensure interoperability between software tools supporting each stage. This model must support distributed system representation in a multi-layered fashion and enable description of any kind of application, thus be extendable. It should also be easily realized in various software tools used to automate discrete configuration stages and facilitate the designer to efficiently provide system specifications.
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DESIGN AND COMPARISON OF RISC PROCESSORS USING DIFFERENT ALU ARCHITECTURES

DESIGN AND COMPARISON OF RISC PROCESSORS USING DIFFERENT ALU ARCHITECTURES

ALU2 (high speed, low power) is built using constraints of parallelism. The Carry Look Ahead adder (CLA) used in this consists of carry generate and propagate terms that are used to precompute the carry thereby increasing its speed [2]. The hardware complexity, though, is very high as can be seen in the results. Hence the area occupied is large. The novelty involved is in the use of Vedic multiplier using Urdhava Triyagbhyam sutra. Further it is known that the conventional Vedic multiplication hardware, has some limitations. Hence to overcome those limitations, a novel approach has been taken with the use of unique ‗addition tree‘ structure to add partially generated products [3]. A 2x2 multiplier is built with basic gates using this sutra as shown in Fig 1. This is then used to create a 4x4 multiplier as shown in Fig 2. This is further expanded to build a 16x16 multiplier. As will be seen in the results, the speed and area occupancy of this multiplier are appreciable making it a viable design for high speed digital signal processing applications.
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Fuzzy rule generation using data mining techniques to classify two-class BCI experiment

Fuzzy rule generation using data mining techniques to classify two-class BCI experiment

developed and different aspects of this new field of research such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. In this paper, a Fuzzy Rule-Based Classification System (FRBCS) is presented in which a novel approach for fuzzy rule generation is proposed. The proposed algorithm makes the use of data mining principles, which are used by frequent pattern mining algorithms. Employing these principles enables us to well generate rules for subsequent classification purposes. Finally, a rule-weighting mechanism is investigated to tune the rule-base to have better classification ability. To evaluate the performance of the proposed scheme features containing standard bandpower and adaptive autoregressive coefficients are determined on four subjects in order to increase the performance of a cue-based BCI system for imagery classification tasks (left and right hand movements). As comparative classifiers, a number of successful methods of classification including Adaboost, Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) have been assessed. The results show that the proposed method of classification is effective in prediction ability of choosing between the left and right imagery tasks.
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Evolving and Ensembling Deep CNN Architectures for Image Classification

Evolving and Ensembling Deep CNN Architectures for Image Classification

Ensembling techniques have been shown to improve deep convolutional neural network (CNN) performance, even when assembled from identical architecture models. GoogleNet [7] used an ensemble of seven identical individuals to reduce the top-5 error rate on the ImageNet ILSVRC 2014 classification challenge dataset from 10.07% to 6.67%, a reduction of 3.45% over their single model result. Szegedy et al. [8] showed that combining similar, but not identical, Inception model architectures also produced improved performance over a single model. Using the Imagenet ILSVRC 2012 classification challenge dataset, they showed a reduction in error rate from 17.8% for the best performing single model, down to 16.4% for an ensemble of four models using two different architecture choices. Performance increases such as these represent an attractive way to improve accuracy scores for a model, as they only require more computational resources, rather than modifications to the model itself. Consequently they can theoretically be performed on any deep CNN, given enough resources. However, this duplication of work represents a linear increase in resource costs, often in exchange for a minor increase in accuracy. Recent works [9], [10] show that ensembles can be constructed effectively without a linear increase in computational work.
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Evolving dataflow visualization to service-oriented architectures on the Grid

Evolving dataflow visualization to service-oriented architectures on the Grid

Scenario: The scenario is a group of computational biologists at different locations around the world who are not skilled in visualization or Grid computing, but want to collaboratively investigate the behaviors of the human heart by running a large number of heart modeling simulations concurrently on a cluster. Simulations are considered “private” to the scientists who are running them, and so they are executed locally on their own computer clusters, keeping all the data confidential. However, they share the resulting visualization and also share in the control of the simulations. During the collaborative session, all the participating scientists use their Web browsers to access the Web portal of a trusted third party static visualization pipeline which will retrieve raw data from simulations and generate a graphic representation of the simulation data. Through the Web portal the scientists can investigate the images of simulations and control the running of simulations, without being concerned about the details of generating the visualization.
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Data Editing for Neuro-Fuzzy Classifiers

Data Editing for Neuro-Fuzzy Classifiers

Data editing techniques have been a subject of several studies [3,11] associated with classifiers using the k- nearest neighbour rule. One of the commonly acknowledged disadvantages of k-nn classifiers is that they require the storage of a large number of samples and that finding k-nearest neighbours for such large training data sets can take too long to compute. However, it has also been observed that a subset of the training set is usually sufficient to approximate very well the decision boundaries. What is more, in cases when training data set contains outliers and noisy data, the use of all training samples in the k-nn classifier design usually lead to worse classification performance (due to overfitting) than when only a suitable subset of the training data is used. The data editing procedures have therefore been applied with the aims of increasing the computational efficiency, through reduction of the number of class prototypes, and improving the generalisation performance through
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Analysis of Different Classifiers for Medical Dataset using Various Measures

Analysis of Different Classifiers for Medical Dataset using Various Measures

Heart disease and Heart attack are one of the major diseases. Heart disease was the major cause of deaths in the different countries including the India. Heart disease kills one person every 34 seconds in the United States. And the cost is about 393.5 billion dollars. Coronary heart disease, and Cardiovascular disease are some categories of heart diseases[8]. The dataset for Heart Cleveland contains 14 attributes and number of instances are 303. Another problem observed in females is breast cancer. It contains total 10 attributes including class attribute and 286 instances. Diabetes dataset contains 9 attributes and 768[7]. India continues to be the "diabetes capital" of the world, and by 2030, nearly 9 per cent of the country's population is likely to be affected with the disease It is estimated that every fifth person with diabetes will be an Indian. This means that India has the highest number of diabetes in any one of the country in the world. WEKA having facility to convert the data sets from arff format into csv format. 10 fold cross validation is used for the evaluation. For constructing the ensemble we are considering base classifiers such as bagging and adaboost in combinations with classifiers such as J48, C4.5, REP tree. Accuracy and time is very important in the field of medical domain, the performance measure accuracy of classification is considered in this study.
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MSAFIS: an evolving fuzzy inference system

MSAFIS: an evolving fuzzy inference system

This study proposed a combination of two algorithms SAFIS and SGD resulting in MSAFIS. Considering the different experiments, this new algorithm provides better compact- ness and higher accuracy compared to the original ones. It is worthwhile to mention, because as MSAFIS as well as SAFIS and SGD are based on online learning, they can han- dle big datasets of any size. They can also be applied to control, prediction, classification, and diagnosis. Here they were successfully used to learn from a challenging dataset of brain and eye signals. As a future work, the stability of the MSAFIS will be analyzed.
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Telecom Network Planning for evolving Network Architectures. Reference Manual

Telecom Network Planning for evolving Network Architectures. Reference Manual

The label forwarding base(LFIB) maintained by an MPLS node consist of a sequence of entries.As illustrated in figure 6.2.4 each entry consists of an incoming label and one or more subentries.the LFIB is indexed by the value contained in the incoming label.each subentry consisits of an outgoing interface and next hop address.subentries within an individual entry may have the same or different outgoing labels. Multicast forwarding requires subentries with multiple outgoing labels,where an incoming packet arriving at one interface needs to sent out on multiple outgoing interfaces.in addition to the outgoing label,outgoing interface and next hop information an entry in the forwarding table may include information related to resources the packet may use such as an outgoing queue that the packet should be placed on.an MPLS node can maintain a single forwarding label per each its interfaces,or a combination of both.in the case of multiple forwarding table instances packet forwarding is handled by the value of incoming label as well as the ingress interface on which the packet arrives.
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Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation

Evolving Image Classification Architectures with Enhanced Particle Swarm Optimisation

Evidenced by recent research [7], [6], [8], [9], CNNs have become exceedingly popular for solving computer vision problems owing to their ability to learn task-specific filters to extract the key information in an image. CNNs are usually comprised of multiple layers of computation that function together to form a network. When designing a CNN for classification, the typical goal is to embed the information found in the image into a fixed length vector, which can then be passed through fully-connected (linear) layers, or even another classifier, to finally output class probabilities. This is performed by cascading layers of convolutional operations with learned filters. There are a number of design and param- eter choices that can be made for each layer, as well as for the connections between the layers. The traditional approach is a purely hierarchical model, where each layer feeds its output feature maps into the next layer sequentially until the final layer is reached. Recent works have, however, explored the possibility of considering the connections between the layers as a Directed Acyclic Graph (DAG), whereby each layer can connect to any number of subsequent layers. This approach is motivated by the success of residual or skip-connections [9], which proved effective at maintaining global features throughout the network by directly passing earlier feature maps to later layers in a hierarchical network. Besides the connections between the layers in the network, each layer itself requires a number of choices to be made pertaining to its parameters and functionality. As previously mentioned, these choices have traditionally been made based on prior knowledge and intuition, with many combinations being thoroughly tested before an optimal architecture is found. There has been a large amount of recent interest in the task of designing architecture search strategies to replace this human-led trial and error process and provide an effective method to automatically design optimal architectures and associated hyper-parameters. As an example, Stanley and Miikulainen [10], [11], [12] explored various methods for automating the process of designing neural network architec- tures. Their method, called NeuroEvolution of Augmenting Topologies (NEAT), took the form of a Genetic Algorithm (GA) which could ‘grow’ architectures from a simple start- ing point. Stanley et al. [13] also explored training neural networks via similar evolutionary methods, although this has thus far not proved to outperform backpropagation. There has
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Changing Architectures and Evolving Urbanism in Modern Japanese Urban Environment

Changing Architectures and Evolving Urbanism in Modern Japanese Urban Environment

II. P OST -W AR J APANESE A RCHITECTURE AND U RBANISM Because of the shock of the defeat and the general shortage of building materials, the main target of architects in the first years of peace time was the construction of safe shelters for the people. In order to tackle the need for fast re-construction, these years witnessed the development of various prefabricated systems for houses made of wood, whose main characteristics were both a very dense interior space and a fast and easy assembly. The theme of the prefabricated housing was to design according to the Modernist concept of “existenz-minimum” (minimum living), a concept that recalls the design of interiors that fit the minimum dimensions for an acceptable standard of living, a feature that was also deeply embedded in the Japanese architectural tradition. Many houses designed during those years were very simple in plan because of the necessity to reduce the costs of production and the time of assembly. Furthermore the attempt to introduce a western style of life suggested using doors instead of shoiji or fusuma (sliding doors) in the separation of the spaces, and avoiding the use of tatami to cover the floors. Moreover, almost all the new houses had a dining-kitchen space, both to stress the more democratic current of Japan and to save more space in the tiny shelters.
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Resolving References to Objects in Photographs using the Words As Classifiers Model

Resolving References to Objects in Photographs using the Words As Classifiers Model

A common use of language is to refer to visually present objects. Modelling it in computers requires modelling the link between language and perception. The “words as classifiersmodel of grounded semantics views words as classifiers of perceptual contexts, and composes the meaning of a phrase through composition of the denotations of its component words. It was recently shown to perform well in a game-playing scenario with a small num- ber of object types. We apply it to two large sets of real-world photographs that contain a much larger variety of object types and for which referring expressions are available. Using a pre-trained convolu- tional neural network to extract image re- gion features, and augmenting these with positional information, we show that the model achieves performance competitive with the state of the art in a reference res- olution task (given expression, find bound- ing box of its referent), while, as we argue, being conceptually simpler and more flex- ible.
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EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Abstract— The paper aims at detecting on-line cognitive failures in driving by decoding the EEG signals acquired during visual alertness, motor-planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor- planning performed by the driver using the pre-processed parietal signals is classified into four classes: braking, acceleration, steering control and no operation. Cognitive failures in motor-planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the co-pilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time- delay between the onset of motor imagination and the EMG response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor-planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor-execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters.
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A Comparative Study on using Principle Component Analysis with different Text Classifiers

A Comparative Study on using Principle Component Analysis with different Text Classifiers

In [19] they try to solve the high dimension of the feature vector problem for text categorization. They use a multistage model to enhance the overall accuracy and the performance of classification. In the first stage, the documents are processed and each document is represented by a bag of words. In the second step each term within the documents are ranked according to their importance for classification using the information gain (IG). Then the third stage is the attribute reduction step based on rough set which is carried out on the terms which are ranked according to their importance. Finally the extracted features are then passed to naive bayes and KNN classifier. They apply their model on three UCI data sets, Reuters-21578, Classic04 and Newsgroup 20. In [6] they try to solve a critical text classification applica- tion, phishing email detection. They use two feature selection techniques - chi-square, information gain ratio and two feature extraction techniques principal component analysis, latent seman- tic analysis are used for extracting the features that improve the classification accuracy. The data set used is prepared by collecting a group of e-mails from the well known publicly available corpus that most authors in this area have used. Phishing data set con- sisting 1,000 phishing emails received from November 2004 to August 2007 provided by Monkey web site and, 1,700 Ham email from Spam Assassin project.
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