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Chủ đề : Information sciences


Có 20+ tài liệu thuộc chủ đề "Information sciences"

PART without the ‘partial’, Comprehensible classifiers, Interpretable models, Partial decision trees

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We choose CHAID ∗ as replacement for C4.5, and propose CHAID ∗ -based UnPART, PART and BFPART algorithms. Results show that C4.5-based Un- PART creates the best classifying models whereas CHAID ∗ -based UnPART creates the sim- plest classifiers. Results show that C4.5-based UnPART has the best discriminating capacity among the compared algorithms.. PART works by partially constructing a C4.5...

Performance evaluation of methods for integrative dimension reduction

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Performance evaluation Dimension reduction Multi-source data Data fusion Matrix factorization. Dimension reduction (DR) methods play an inevitable role in analyzing and visualizing high-dimensional multi-source data. Our ranking is obtained based on four main factors: quality of dimension reduction (local, global, and local-global neighborhood preservation), cluster- ing quality, speed and sensitivity to input parameters on multiple datasets generated by InterSIM (a...

Heterogeneous linear multi-agent consensus with nonconvex input constraints and switching graphs

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Heterogeneous linear multi-agent consensus with nonconvex input constraints and switching graphs. Heterogeneous multi-agent systems Control input constraints Switching graphs Distributed algorithm. This study focuses on a constrained consensus problem of heterogeneous linear multi- agent systems with nonconvex input constraints and switching graphs. A distributed con- trol algorithm with the time-varying gain is presented to ensure the control input of each...

No-wait two-stage flowshop problem with multi-task flexibility of the first machine

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No-wait two-stage flowshop problem with multi-task flexibility of the first machine. Flowshop No-wait Blocking. Operating room and recovery room are two of the most resource-intensive areas in a hospital. For the purpose of reducing cost while maintaining a good quality of care, effective scheduling of operation room beds and recovery room beds has become one of the major priorities.. the...

Objective function-based rough membership C-means clustering

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Hard C-means Rough set theory Rough C-means Rough set C-means Rough membership C-means Objective function. Hard C-means (HCM) is one of the most widely used partitive clustering methods and was extended to rough C-means (RCM) by referencing to the perspective of rough set theory to deal with the certain, possible, and uncertain belonging of object to clusters. Furthermore, rough set...

Influence maximization algorithm based on Gaussian propagation model

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Influence maximization algorithm based on Gaussian propagation model. Social network Influence maximization Gaussian propagation model Greedy algorithm. The influence of each entity in a network is a crucial index of the network information dis- semination. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. The results of the...

Using argumentation in expert’s debate to analyze multi-criteria group decision making method results

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Using argumentation in expert’s debate to analyze multi-criteria group decision making method results q. Multi-criteria group decision making Consensus measures. Recent multi-criteria group decision making methods focus their analysis on the experts preferences. Also, the proposed method allows us to determine which are the arguments that most of the experts have followed. Multi-criteria group decision making methods are an interesting...

An evolutionary framework for detecting protein conformation defects

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Due to the simplicity and efficiency of the histogram based techniques, the histogram based approaches are widely used for image analysis. Following the proposed CDF-DRVC methodology, we introduce a methodology for synthesizing feature functions, expressed in highly nonlinear functions of the CDF-DRVC basic features, by means of the GP and the EM algorithm (GP-EM) for detecting OPMD. The main objectives...

Fuzzy system reliability analysis based on level (k, 1) interval-valued fuzzy numbers

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Fuzzy system reliability analysis based on level (k, 1) interval-valued fuzzy numbers. Reliability Fuzzy number. Level (k, 1) interval-valued fuzzy numbers Fuzzy reliability. This study uses Level (k, 1) interval-valued fuzzy numbers to examine the fuzzy reliability of a serial system and a parallel system and obtain the estimated reliability of both systems in the fuzzy sense.. The probability assumption...

Population-based algorithm portfolios with automated constituent algorithms selection

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Population-based Algorithm Portfolios with automated constituent algorithms selection. Previous investigation on PAP reveals that choosing appropriate constituent algorithms is crucial to the success of PAP. EPM- PAP is equipped with a novel constituent algorithms selection module, which is based on the EPM of each candidate EAs. Empirical studies demonstrate that the EPM-based selection method can successfully identify appropriate constituent EAs,...

Analysis of fitness landscape modifications in evolutionary dynamic optimization

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It is important to observe that some of the fitness transformations analysed here, like those caused by the duplication of solutions, are not currently explored in the evolutionary dynamic optimization area.. By changing the number of peaks and the dimension of the search space, the difficult of the problem is controlled. By modifying the frequency and severity of changes, the...

Generic ontology of datatypes

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Especially in data mining research it is impossible to efficiently (semi-) automatically connect parts of workflows, such as data preprocessing and data mining, perform analysis of the research results and communicate the research outputs, without machine process- able representation of datatypes and their properties. OntoDT defines the semantics, i.e., meaning of the key entities and represents the knowledge about datatypes...

Dynamic higher-order cumulants analysis for state monitoring based on a novel lag selection

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However, the key step of dynamic state monitoring meth- ods is determination of the time lags or the lag structure. Meanwhile, conventional univariate statistical process monitoring (SPM) methods are not suitable in this situation because of high correlations of the variables. Rato’s method takes full advantage of the singular value of the covariance matrix of the data. Moreover, the continuous...

BILU-NEMH: A BILU neural-encoded mention hypergraph for mention extraction

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Some of the fundamental research in NLP includes the named en- tity recognition, which recognizes the named entities (i.e., persons and companies) from texts, the semantic parsing, which converts a natural language utterance to a logical form, and the co-reference resolution, which extracts the nouns (including pronouns and noun phrases) pointing to the same reference body. Although the above models...

DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring

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The Statlog German (10 0 0 instances) credit approval dataset available in the UCI machine learning repository is used to test the effectiveness of our model in the CS do- main. is the best prediction performance for this well-known credit scoring dataset, compared to the existing work in the field.. Financial risk management is one of the most sensitive subjects...

Why you should stop predicting customer churn and start using uplift models

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Therefore, in this paper, we introduce a novel evaluation metric called the maximum profit uplift (MPU) measure that allows assessing the performance in terms of the maxi- mum potential profit that can be achieved by adopting an uplift model. While introducing the MPU mea- sure, we describe the generally applicable liftup curve and liftup measure for evaluating uplift models as...

Asymptotic resolution bounds of generalized modularity and multi-scale community detection

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Detecting such community structures can be viewed as partitioning of the network into clusters in which the nodes are more densely connected to each other than to the nodes in the rest of the network. Modularity maximization [19] is one of the state-of-the-art methods for community detection.. It aims at discovering the partition of the network that maximizes modularity, a...

Fuzzy implications: Alpha migrativity and generalised laws of importation

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Fuzzy implications: alpha migrativity and generalised laws of importation. Fuzzy connectives T-norm Fuzzy implication Law of importation Alpha-migrativity. In this work, we discuss the law of α -migrativity as applied to fuzzy implication func- tions in a meaningful way. A generalisation of this law leads us to Pexider-type func- tional equations connected with the law of importation, viz., the generalised...

Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery

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Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery. Low-rank tensor recovery (LRTR) Mode-k 1 k 2 tensor unfolding Tensor N-tubal rank. To tackle these two issues, we define a new tensor unfolding operator, named mode-k 1 k 2 tensor unfolding, as the process of lexicographically stacking all mode-k 1 k 2 slices of an N-way tensor into...

Differentially private publication of streaming trajectory data

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This framework is designed to achieve high data utility, while effec- tively ensuring the preservation of privacy in the published results. The second module is VCR comprising three algorithms based on differential privacy to facilitate the publication of the distribution of position statistics. 2(b)) to the public. Specifically, we demonstrate how we can ensure users’ privacy while releasing their streaming...