scholarly journals Ensemble-Based Neighborhood Attribute Reduction: A Multigranularity View

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yuan Gao ◽  
Xiangjian Chen ◽  
Xibei Yang ◽  
Pingxin Wang ◽  
Jusheng Mi
Keyword(s):  
Classification Accuracy ◽  
Granular Computing ◽  
Attribute Reduction ◽  
Data Sets ◽  
Time Efficiency ◽  
Elapsed Time ◽  
Interesting Topic ◽  
Traditional Process ◽  
Different Levels

Recently, multigranularity has been an interesting topic, since different levels of granularity can provide different information from the viewpoint of Granular Computing (GrC). However, established researches have focused less on investigating attribute reduction from multigranularity view. This paper proposes an algorithm based on the multigranularity view. To construct a framework of multigranularity attribute reduction, two main problems can be addressed as follows: (1) The multigranularity structure can be constructed firstly. In this paper, the multigranularity structure will be constructed based on the radii, as different information granularities can be induced by employing different radii. Therefore, the neighborhood-based multigranularity can be constructed. (2) The attribute reduction can be designed and realized from the viewpoint of multigranularity. Different from traditional process which computes reduct through employing a fixed granularity, our algorithm aims to obtain reduct from the viewpoint of multigranularity. To realize the new algorithm, two main processes are executed as follows: (1) Considering that different decision classes may require different key condition attributes, the ensemble selector is applied among different decision classes; (2) to accelerate the process of attribute reduction, only the finest and the coarsest granularities are employed. The experiments over 15 UCI data sets are conducted. Compared with the traditional single-granularity approach, the multigranularity algorithm can not only generate reduct which can provide better classification accuracy, but also reduce the elapsed time. This study suggests new trends for considering both the classification accuracy and the time efficiency with respect to the reduct.

The Incremental Knowledge Acquisition Based on Hash Algorithm

2016 ◽  
Vol 24 (03) ◽  
pp. 347-366 ◽  
Author(s):  
Qing-Hua Zhang ◽  
Long-Yang Yao ◽  
Guan-Sheng Zhang ◽  
Yu-Ke Xin
Keyword(s):  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Algorithm Analysis ◽  
Data Sets ◽  
Hash Algorithm ◽  
Acquisition Method

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

Combined Accelerator for Attribute Reduction: A Sample Perspective

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Chen ◽  
Jingjing Song ◽  
Keyu Liu ◽  
Yaojin Lin ◽  
Xibei Yang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Data Sets ◽  
Multiple Views ◽  
Time Consumption ◽  
Elapsed Time ◽  
Rough Approximation ◽  
Neighborhood Rough Set ◽  
Speed Up ◽  
Neighborhood Relation

In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views.

scholarly journals Neighborhood Based Multi-Granularity Attribute Reduction: An Acceleration Approach

2020 ◽  
Author(s):  
Jingjing Song ◽  
Huili Dou ◽  
Xiansheng Rao ◽  
Xiaojing Luo ◽  
Xuan Yan
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Superior Performance ◽  
Data Sets ◽  
Feature Selection Technique ◽  
Elapsed Time ◽  
Multiple Granularities ◽  
Naive Approach

As a feature selection technique in rough set theory, attribute reduction has been extensively explored from various viewpoints especially the aspect of granularity, and multi-granularity attribute reduction has attracted much attention. Nevertheless, it should be pointed out that multiple granularities require to be considered simultaneously to evaluate the significance of candidate attribute in the corresponding process of computing reduct, which may result in high elapsed time of searching reduct. To alleviate such a problem, an acceleration strategy for neighborhood based multi-granularity attribute reduction is proposed in this paper, which aims to improve the computational efficiency of searching reduct. Our proposed approach is actually realized through the positive approximation mechanism, and the processes of searching qualified attributes are executed through evaluating candidate attributes over the gradually reduced sample space rather than all samples. The experimental results over 12 UCI data sets demonstrate that the acceleration strategy can provide superior performance to the naive approach of deriving multi-granularity reduct in the elapsed time of computing reduct without generating different reducts.

scholarly journals About a Distance Measure and Application for Finding Reduct in Incomplete Decision Tables

2019 ◽  
Vol 9 (1) ◽  
pp. 6294-6298
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Distance Measure ◽  
Attribute Reduction ◽  
Data Sets ◽  
Reduction Methods ◽  
Tolerance Rough Set ◽  
Decision Tables ◽  
Filter Algorithms ◽  
Over 40 Years

Tolerance rough set model is an effective tool to reduce attributes in incomplete decision tables. Over 40 years, several attribute reduction methods have been proposed to improve the efficiency of execution time and the number of attributes of the reduct. However, they are classical filter algorithms, in which the classification accuracy of decision tables is computed after obtaining the reducts. Therefore, the obtained reducts of these algorithms are not optimal in terms of reduct cardinality and classification accuracy. In this paper, we propose a filter-wrapper algorithm to find a reduct in incomplete decision tables. We then use this measure to determine the importance of the property and select the attribute based on the calculated importance (filter phase). In the next step, we find the reduct with the highest classification accuracy by iterating over elements of the set containing the sequence of attributes selected in the first step (wrapper phase). To verify the effectiveness of the method, we conduct experiments on 6 famous UCI data sets. Experimental results show that the proposed method increase classification accuracy as well as reduce the cardinality of reduct compared to Algorithm 1 [12].

scholarly journals Fuzzy Distance Based Attribute Reduction in Decision Tables

2017 ◽  
pp. 104
Author(s):  
Cao Chinh Nghia ◽  
Vu Duc Thi ◽  
Nguyen Long Giang ◽  
Tan Hanh
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Data Sets ◽  
Fuzzy Rough Set ◽  
Fuzzy Distance ◽  
Reduction Methods ◽  
Decision Tables ◽  
Attribute Value

In recent years, fuzzy rough set based attribute reduction has attracted the interest of many researchers. The attribute reduction methods can perform directly on the decision tables with numerical attribute value domain. In this paper, we propose a fuzzy distance based attribute reduction method on the decision table with numerical attribute value domain. Experiments on data sets show that the proposed method is more efficient than the ones based on Shannon’s entropy on the executed time and the classification accuracy of reduct.

scholarly journals Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4418 ◽  
Author(s):  
Aleksandra Sekrecka ◽  
Michal Kedzierski
Keyword(s):  
High Ratio ◽  
Data Sets ◽  
Spectral Quality ◽  
Local Statistics ◽  
Panchromatic Image ◽  
Urbanized Area ◽  
Fusion Methods ◽  
Spatial Quality ◽  
Different Levels

Commonly used image fusion techniques generally produce good results for images obtained from the same sensor, with a standard ratio of spatial resolution (1:4). However, an atypical high ratio of resolution reduces the effectiveness of fusion methods resulting in a decrease in the spectral or spatial quality of the sharpened image. An important issue is the development of a method that allows for maintaining simultaneous high spatial and spectral quality. The authors propose to strengthen the pan-sharpening methods through prior modification of the panchromatic image. Local statistics of the differences between the original panchromatic image and the intensity of the multispectral image are used to detect spatial details. The Euler’s number and the distance of each pixel from the nearest pixel classified as a spatial detail determine the weight of the information collected from each integrated image. The research was carried out for several pan-sharpening methods and for data sets with different levels of spectral matching. The proposed solution allows for a greater improvement in the quality of spectral fusion, while being able to identify the same spatial details for most pan-sharpening methods and is mainly dedicated to Intensity-Hue-Saturation based methods for which the following improvements in spectral quality were achieved: about 30% for the urbanized area and about 15% for the non-urbanized area.

scholarly journals Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing

2020 ◽  
Author(s):  
Kashyap Chhatbar ◽  
Justyna Cholewa-Waclaw ◽  
Ruth Shah ◽  
Adrian Bird ◽  
Guido Sanguinetti
Keyword(s):  
Alternative Splicing ◽  
Dna Sequences ◽  
Molecular Mechanisms ◽  
Data Sets ◽  
Learning Approaches ◽  
Global Regulator ◽  
Major Function ◽  
Quantitative Analyses ◽  
Different Levels

AbstractMeCP2 is an abundant protein in mature nerve cells, where it binds to DNA sequences containing methylated cytosine. Mutations in the MECP2 gene cause the severe neurological disorder Rett syndrome (RTT), provoking intensive study of the underlying molecular mechanisms. Multiple functions have been proposed, one of which involves a regulatory role in splicing. Here we leverage the recent availability of high-quality transcriptomic data sets to probe quantitatively the potential influence of MeCP2 on alternative splicing. Using a variety of machine learning approaches that can capture both linear and non-linear associations, we show that widely different levels of MeCP2 have a minimal effect on alternative splicing in three different systems. Alternative splicing was also apparently indifferent to developmental changes in DNA methylation levels. Our results suggest that regulation of splicing is not a major function of MeCP2. They also highlight the importance of multi-variate quantitative analyses in the formulation of biological hypotheses.

Creating Open-Source Interactive Articles for the Wider Publics

2015 ◽  
pp. 407-429
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Open Access ◽  
Open Source ◽  
Value Added ◽  
Data Sets ◽  
Authoring Tools ◽  
Levels Of Abstraction ◽  
Digital Photographs ◽  
Multiple Audiences ◽  
Electronic Games ◽  
Different Levels

A core form of the international sharing of research and analysis is done through articles, both those presented in live conferences and those published in any number of journals. Interactive articles integrate various elements to the basic text: hyperlinks; immersive simulations; electronic games; data sets; knowledge collections; digital photographs; multimedia; integrated wikis and blogs; and other aspects. These value-added pieces that build exploration, experience, and interactivity, are enabled by current authoring tools and Web servers and open-source contents. Enriched articles often encourage return engagements, and their open-source publishing often leads to greater levels of citations and readership. These enable the design of a work for multiple audiences, with opt-in sections for different levels of readers, for example. Interaction enables opportunities for more reflection, recursiveness, and understanding a topic from multiple angles and different levels of abstraction. Interactive articles tend to appear in open-source (or at least open-access) publications online, which enables access by wider reading publics and machine-searchability and often wider citations.

Performance of Negative Selection Algorithms in Patient Detection and Classification

2018 ◽  
pp. 78-102
Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz
Keyword(s):  
Immune System ◽  
Error Detection ◽  
Classification Accuracy ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Detection Accuracy ◽  
Data Sets ◽  
Selection Algorithm ◽  
Artificial Immune

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.

Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy

2011 ◽  
pp. 91-109
Author(s):  
Amit Saxena ◽  
John Wang
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Fitness Function ◽  
Synthetic Data ◽  
Feature Subset Selection ◽  
Second Phase ◽  
Data Sets ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Two Phase

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Error as the fitness function. Different subsets of features are obtained. In the second phase, each of the reduced features set is applied to test the CA of the dataset. The CA of a data set is validated using supervised k-nearest neighbor (k-nn) algorithm. The novelty of the proposed scheme is that each reduced feature set obtained in the first phase is investigated for CA using the k-nn classification with different Minkowski metric i.e. non-Euclidean norms instead of conventional Euclidean norm (L2). Final results are presented in the paper with extensive simulations on seven real and one synthetic, data sets. It is revealed from the proposed investigation that taking different norms produces better CA and hence a scope for better feature subset selection.

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