Different Types Of Attributes Research Articles

Taking into account Qualitative and Textual Variables in Hierarchical Ascending Clustering (HAC)

In Machine Learning, the clustering methods are the mains unsupervised methods. Their objectives is to partition a set of objects in some homogeneously groups. Clustering methods in general and more particularly Hierarchical Ascending Clustering (HAC) techniques are based on metrics and ultra-metrics. Metrics are used to evaluate the similarities between two objects; and ultra-metrics are used to estimate the similarity of two groups or the similarity of an element and a group. The main characteristic of these metrics and ultra-metrics is the fact that they are only adapted to numerical variables or can be reduced to them. With the advent of Data Mining and Data Science, most of the datasets to be analyzed contain different types of variables. In the same dataset, we can find numeric attributes, qualitative variables and free text fields very often together. Despite this diversity of variables in the same dataset, the existed clustering methods are generally build to use only an unique kind of attribute. In this paper, we propose an approach to take account different types of attributes in the same clustering method. The method proposed is a variant of HAC methods that can take into account both numerical, qualitative and textual data. Our approach is based on a metric call Phi-Similarity we developed in order to estimate the proximity of two objects, each of them is describe by a vector of attributes of different types. The developed method will be implemented with the scientific computing language R and applied to real survey data. A comparison of the results will be made with HAC techniques based on classical metrics with the Ward criterion as aggregation criteria. For classical algorithms, we will limit ourselves to the variables of the database compatible with them. This work of comparison will highlight the gain in precision in terms of classification brought by our method compared to the classic versions of HAC

Research on Lost Circulation Risk Intelligent Identification and Processing System Based on CBR

Due to the large number of complex and uncertain factors in the drilling process, it is very difficult to identify and deal with the risk of lost circulation by establishing an accurate mathematical model, and the existing intelligent methods are difficult to acquire or require large sample data. In view of the defects, an intelligent decision-making method based on case-based reasoning is proposed, and a software system for intelligent identification and processing of lost circulation risk is developed. In this system, the lost circulation cases of oil fields in the past are represented as case forms in the case base. The design idea of the case retrieval model is to design an improved similarity calculation model for different types of attributes, based on the traditional instance similarity calculation model, which can effectively solve the information uncertainty. The system was tested by field data and the results show that the lost circulation risk identification and processing method can meet the application requirements of the drilling site, and can effectively improve the intelligent level of lost circulation risk management.

The strategy of traffic congestion management based on case-based reasoning

This paper proposes a case-based reasoning (CBR) method for traffic congestion management in view of the rapid development of urban motorization and the increasingly prominent problem of traffic congestion. The reasoning model based on CBR congestion management is established, and the characteristic attributes of traffic congestion cases are analyzed. The calculation methods combining local and global similarity are adopted for different types of attributes. Meanwhile, it proposes the update and preservation mode for traffic congestion case database. The cases indicate that traffic congestion management can quickly find a solution to traffic congestion problem by calculating the similarity between congestion cases through CBR. The cases prove that this method can improve the accuracy of CBR results and have certain guiding significance for traffic management.

GENRE AND STYLISTIC CHARACTERISTICS OF NOUN ATTRIBUTES IN GERMAN TOWN CHRONICLES OF THE XV AND XVI CENTURIES

The paper deals with the actual issue of the historical stylistics of German development of the nominal style in the Early New High German (XVth and XVIth centuries). The town chronicles as one of the most popular genres of the narrative prose from this period are used as study material. For the research, four chronicles of the German-language area from XVth and XVIth centuries have been chosen: these of Bern, Basel, Worms and Zrich. The purpose of article is to analyze different types of attributes with regard to their genre-specific function. First, an analysis of syntactic and semantic types of attributes in each chronicle is conducted with subsequent data comparison between each text and conclusions about stylistic and pragmatic factors of the use of attributes. For this, we used a sample of 1500 noun phrases chosen from the chronicles specified.
 The action nouns realize their combinative potential most often. The most frequent attribute type is an attribute in Genitive, whereby some differences in its topology are discovered between the chronicles of the XVth century, on the one side, and the chronicles of the XVIth century, on the other. In the latters, the attributive genitive is more often placed after the main word. For all chronicles, only a small number of infinitives and participles in function of attribute are found, the number of noun phrases with several attributes is small, too. The percentage of noun phrases with attributes of the second and third dependency degree is not large either.
 The research made allows a conclusion that the town chronicles have differed from the usage of official writing in respect to the volume and depth of a noun phrase. Differences between the chronicles of XVth and XVIth centuries are discovered only in some attribute types, whereas the general picture does not alter principally. The noun phrase in our texts should be characterized as a compact one. It may be explained with the unwillingness of authors to complicate the perception of their texts, for many official chronicles were read aloud. Complexity and informativeness characteristic for the official and early scientific texts seem to be expressed in chronicles at the level of the main sentence parts, but not at that of a noun phrase.

Between acknowledgement and countering: Interpersonal functions of English reportative adverbs

This study focuses on examining interpersonal functions of two English reportative adverbs: reportedly and allegedly. In Martin and White’s (2005) typology of “resources of intersubjective positioning,” which this work employs, reportative adverbs are associated with the function of attribution, as they are used to “attribute the proposition to some external source” (Martin and White, 2005: 111). The aim of this study is to establish the contexts in which they signal different types of attribution (acknowledgement and distancing), and to identify any additional functions they perform. Most of the illustrative examples come from the British National Corpus and the Corpus of Contemporary American English. The type of attribution is demonstrated to depend on such factors as the syntactic position of the adverb, the type of sentence it occurs in, the amount of information given in the context and its status. Allegedly is shown to additionally perform a countering function. It is used to challenge the interlocutor’s right to claim knowledge and to undermine the reliability of the interlocutor’s claims. Reportative adverbs are thus shown to indicate both the status of the information presented by the speaker and the speaker’s position towards the information presented by the interlocutor.

Do top management performance attribution patterns matter to subsequent organizational outcomes? A two-country study of attribution in economic crisis

AbstractTop management plays a central role in interpreting the organization’s performance and communicating to stakeholders. Management’s performance attributions set the tone for accountability and shape organizational climate. However, prior studies of the relationships between attribution patterns and subsequent performance have been limited and reported mixed results. This study extends this literature stream by examining the relationships of four different types of attribution and subsequent performance concurrently and testing them in a two-country sample. We find a significant positive relationship between positive internal attribution and subsequent performance, and reaffirm prior findings on negative external attribution and performance.

Performance Analysis of KNN on Different Types of Attributes

Data Mining is an inter-disciplinary promising field that focuses on access of information useful for high level decisions and also includes Machine Learning. Data Miners evaluate and filter the data as a result and convert the data into useful information. The useful information is converted into knowledge by performing some techniques. The K-Nearest Neighbor (K-NN) algorithm is an instance based learning method that has been widely used in many pattern classification tasks due to its simplicity, effectiveness and robustness. This paper presents a performance comparison of KNN algorithm in various data sets that includes different types of attributes. The results of this paper are achieved using WEKA tool.

I believe; therefore, I work harder: The significance of reflective thinking on effort-making in academic failure in a Confucian-heritage cultural context

According to research, East Asian students tend to persist after academic failure. However, little research has explored the underlying mechanism of such persistence from the perspective of reflective thinking. Effort-related beliefs may play a key role in the reflection process. This study examined how effort beliefs affect attribution and affect, thereby contributing to students’ persistence in academic failure in the Confucian-heritage cultural context. A sample of 281 Taiwanese 10th graders was assessed concerning their effort beliefs, attribution of academic failure, negative emotions, and effortful behavior. Structural equation modeling was used to analyze the data. The results indicated that those holding different effort beliefs experience different types of attribution and emotions. Improvement-oriented beliefs about effort were found to be associated with a lack of effort and induce participants’ indebtedness towards parents and self, which in turn motivates them to persist after academic failure. Obligation-oriented beliefs about effort trigger two paths, one through a sense of indebtedness that prompts subsequent effort-making and the other directly impacting such effortful actions without feelings of indebtedness. Theoretically, this study uncovered the psychological mechanism of reflective thinking after academic failure in a Confucian society such as Taiwan. Practically, the findings highlighted that constructive reflection after failure may provide students with strong motivation to persist and an opportunity for building character in the process of achieving future academic success.

Cognitive Weighted Inherited Class Complexity Metric

In the software industry, the class complexity of an object oriented programis measured using various factors such as counting the number of lines of code, methods, attributes, functional points, etc.,. Many researchers propose the class complexity metrics for object oriented design. Aloysius et al., has proposed a class complexity metric called Attribute Weighted Class Complexity (AWCC) from Extended Weighted Class Complexity (EWCC).The metric AWCC was calculated by adding the complexity of methods, attributes and complexity due to inheritance. The main limitation of AWCC is that on calculating inherited complexity, the Cognitive Load (CL) assigned to Lth level in inheritance hierarchy was assumed to be 1 always, but the cognitive load to understand the different levels ofinheritance can’t be same. So, there is a need for a metric to overcoming the limitations of AWCC. Hence, a new metric namely Cognitive Weighted Inheritance Class complexity (CWICC) is proposed in this paper. In CWICC, cognitive weights are assigned for each level of inheritance and also calculate the complexity due to the reusing of different types of attributes. The proposed metric has been validated empirically and also theoretically by using the properties of Weyuker’s. The result is matched with otherexisting measures and it shows that the proposed metric is to be a more appropriate indicator to measure the complexity of the class.

Measuring the components of visitor experience on a mountain attraction: the case of the Nordkette, Tyrol, Austria

PurposePrevious mountain tourism research addressed economic, environmental, social and political impacts. Because limited studies evaluated visitors’ perception of their experience, this study aims to examine the tangible and intangible visitor experience in a Tyrolean alpine tourist attraction.Design/methodology/approachThe study adopted Klaus and Maklan’s (2012) customer experience model, suggesting that customers base their experience perception on the quality of product experience, outcome focus, moments of truth and peace-of-mind. Their model was used to validate the impact on overall customer experience quality at the mountain attraction through conducting a structured survey with 207 face-to-face interviews on-site.FindingsThe results of the confirmatory factor analysis did not confirm the four-dimensional structure, probably due to the differences between mountain tourism experience and the mortgage lending experience in the original study. Instead, principal component analysis suggested a different dimensional structure of components that were arbitrarily named as functional, social, comparative and normative aspects of the visitors’ experience.Research limitations/implicationsThe results are based on a sample in a given period of time, using convenience sampling. While the sample size satisfied the data analysis requirements, confirmatory factor analysis would benefit from a larger sample size.Practical implicationsConsumer experience dimensions while visiting a mountain attraction may not be concrete or objective, and consequently may yield different types of attributes that influence behavior.Social implicationsThe social exchange theory could explain relationships between visitors and service providers and their consequences. Attraction managers should increase benefits for visitors and service providers to enhance their relationships, and thus experience.Originality/valueThe study explored the applicability of an existing experiential consumption model in a mountain attraction context. The findings introduce a revised model that may be applicable in other tourist attractions.

Effects of Media Attributions on Responsibility Judgments and Policy Opinions

This study tests how the public’s responsibility judgments and their policy opinions could be affected by different types of attributions made by the media. Study 1 examined three types of attributions regarding smartphone addiction: (a) individualistic (e.g., lack of self-control), (b) societal (e.g., the media industry or government), and (c) none (i.e., control group). Results showed that individualistic attributions by the media did not affect perceived individual responsibility and support for smartphone-addicted children. On the other hand, societal attributions increased perceived industry responsibility, which subsequently increased punishment opinions for the smartphone industry. Study 2 examined three types of attributions regarding childhood obesity (child vs. parent vs. societal) by two types of exemplar formats (“interviews” vs. “user comments”). Consistent with Study 1, results showed that the societal attribution condition resulted in greater perceived industry responsibility and greater punishment opinions for the food industry. However, the effect was not moderated by the type of exemplar format.

Radar emitter classification for large data set based on weighted‐xgboost

Radar emitter classification (REC) is very important in both civil and military fields. It becomes more and more difficult to classify the intercepted radar signals with the increasing complexity of radar signals. An efficient classification method using weighted-xgboost (w-xgboost) model for the complex radar signals is proposed in this study. The xgboost method is widely used by data scientists and performs very well in many machine learning projects. The authors use a large data set which consists of different types of attributes (such as continuous data, categorical data, and discrete data) to train the model. A smooth weight function is introduced to solve the data deviation problem. Experiment results show that the authors' w-xgboost method achieves a better performance than several existing machine learning algorithms on the test set.

A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis.

We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts. We therefore used a distance measure named weighted heterogeneous value distance metric, which can better deal with both continuous and discrete attributes simultaneously than the standard Euclidean distance, and a genetic algorithm for learning the attribute weights involved in this distance measure automatically. We evaluated our CBR system in two case studies, related to benign/malignant tumor prediction and secondary cancer prediction, respectively. Weighted heterogeneous value distance metric with genetic algorithm for weight learning outperformed several alternative attribute matching methods and several classification methods by at least 3.4%, reaching 0.938, 0.883, 0.933, and 0.984 in the first case study, and 0.927, 0.842, 0.939, and 0.989 in the second case study, in terms of accuracy, sensitivity×specificity, F measure, and area under the receiver operating characteristic curve, respectively. The evaluation result indicates the potential of CBR in the breast cancer diagnosis domain.

Конструкції в англійській мові: від парадигматичних до синтагматичних відношень

The paper argues that the present-day Construction Grammar approaches mainly focus on the paradigmatic relations concerning different levels of generalizing constructions and suggests that Construction Grammar take into account syntagmatic relations of the structures singled out by paradigmatic studies. The article differentiates five types of syntagmatic constructions in the English language: immediate, consisting of two or three words; modified with different types of attributes; extended comprising several immediate constructions within one utterance; supersyntactic related across two utterances; textual occurring throughout three and more utterances.

Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy

Feature selection in the data with different types of feature values, i.e., the heterogeneous or mixed data, is especially of practical importance because such types of data sets widely exist in real world. The key issue for feature selection in mixed data is how to properly deal with different types of the features or attributes in the data set. Motivated by the fuzzy rough set theory which allows different fuzzy relations to be defined for different types of attributes to measure the similarity between objects and in view of the effectiveness of entropy to measure information uncertainty, we propose in this paper a fuzzy rough set-based information entropy for feature selection in a mixed data set. It is proved that the newly-defined entropy meets the common requirement of monotonicity and can equivalently characterize the existing attribute reductions in the fuzzy rough set theory. Then, a feature selection algorithm is formulated based on the proposed entropy and a filter-wrapper method is suggested to select the best feature subset in terms of classification accuracy. An extensive numerical experiment is further conducted to assess the performance of the feature selection method and the results are satisfactory.

Efficient parallel boolean matrix based algorithms for computing composite rough set approximations

In information systems, there may exist multiple different types of attributes like categorical attributes, numerical attributes, set-valued attributes, interval-valued attributes, missing attributes, etc. Such information systems are called as composite information systems. To process such attributes with rough set theory, composite rough set model and corresponding matrix methods were introduced in our previous study. Calculation of rough set approximations of a concept is the key step for rule acquisition and attribute reduction in rough set based methods. To accelerate the computation process of rough set approximations, this paper first presents the boolean matrix representation of the lower and upper approximations in the composite information system, then designs a parallel method for computing approximations based on matrix, and implements it on Multi-GPU. The experiments on data sets from UCI and user-defined data sets show that the proposed method can accelerate the computation process efficiently. The Multi-GPU implementation achieves up to a speedup of 334.9 over the CPU implementation.

Attribute Reduction for Heterogeneous Data Based on the Combination of Classical and Fuzzy Rough Set Models

Attribute reduction with rough sets aims to delete superfluous condition attributes from a decision system by considering the inconsistency between condition attributes and the decision labels. However, heterogeneous condition attributes including symbolic and real-valued ones always coexist for most decision systems and different types of attributes induce different kinds of granular structures. The existing rough set models do not have explicit mechanisms to address different kinds of granular structures reasonably and effectively. In this paper, we aim to perform attribute reduction for decision systems with symbolic and real-valued condition attributes by composing classical rough set and fuzzy rough set models. We first define a discernibility relation for every symbolic and real-valued condition attribute to characterize its discernible ability related to the decision labels. With these discernibility relations, we can develop a dependence function to measure the inconsistency between heterogeneous condition attributes and decision labels, and attribute reduction aims to keep this dependence function with a small perturbation. The proposed attribute reduction deals with heterogeneous condition attributes from the viewpoint of discernible ability and can consider the mutual effects between two types of attributes without preprocessing into single-typed ones. An algorithm to find reducts is developed and experiments are performed to demonstrate that the proposed idea is effective.

MV5: A Clinical Decision Support Framework for Heart Disease Prediction Using Majority Vote Based Classifier Ensemble

The medical diagnosis process can be interpreted as a decision-making process during which the physician induces the diagnosis of a new and unknown case from an available set of clinical data and using his/her clinical experience. This process can be computerized in order to present medical diagnostic procedures in a rational, objective, accurate and efficient way. In the last few decades, many researchers have focused on developing effective methods for intelligent heart disease prediction and decision support systems. For such a system, high accuracy of prediction is paramount. In this research, an ensemble classifier is proposed, which uses majority vote-based scheme for heart disease data classification and prediction. The five heterogeneous classifiers used to construct the ensemble model are as follows: Naive Bayes, decision tree based on Gini Index, decision tree based on information gain, memory-based learner and support vector machine. Five datasets from different data repositories are employed for testing the effectiveness of the ensemble model. Each dataset has different types of attributes, for instance binary, real, continuous, categorical, etc. Experimental results with stratified cross validation show that the proposed MV5 framework deals with all the attribute types. MV5 has achieved an accuracy of 88.52% with 86.96% sensitivity, 90.83% specificity and 88.85% f-measure. Comparison of proposed MV5 model with individual classifiers shows increase in average accuracy, sensitivity, specificity and f-measure of about 14, 11, 17 and 18% respectively.

Identification of unknowns within a probabilistic system: The diagnostic value of attributes

Using a data base underpinned by probability considerations in which a variety of attributes, some of which may be quantitative, are recorded for a number of “operational taxonomic units” (OTUs), a key system is described by which an unnamed specimen may quickly be identified. The concept of “diagnostic power” is introduced, by which each attribute is evaluated in terms of its potential contribution to identifying the unnamed specimen. Besides coverage of different types of attributes and the introduction of “diagnostic power”, the system has the advantages of incorporating multiple values of an attribute for each OTU and offering short-cuts to identification.

Multi-attribute auctions with different types of attributes: Enacting properties in multi-attribute auctions

Multi-attribute auctions allow agents to sell and purchase goods and services taking into account more attributes than just price (e.g. service time, tolerances, qualities, etc.). In this paper we analyze attributes involved during the auction process and propose to classify them between verifiable attributes, unverifiable attributes and auctioneer provided attributes. According to this classification we present VMA2, a new Vickrey-based reverse multi-attribute auction mechanism, which takes into account the different types of attributes involved in the auction and allows the auction customization in order to suit the auctioneer needs. On the one hand, the use of auctioneer provided attributes enables the inclusion of different auction concepts, such as social welfare, trust or robustness whilst, on the other hand, the use of verifiable attributes guarantee truthful bidding. The paper exemplifies the behavior of VMA2 describing how an egalitarian allocation can be achieved. The mechanism is then tested in a simulated manufacturing environment and compared with other existing auction allocation methods.