Attribute Reduction Research Articles

Powder dosing within continuous manufacturing: A lean approach for gravimetric dosing configuration and equipment selection

This work presents a novel approach to select gravimetric dosing configurations for continuous unit operations in pharmaceutical processing. It optimizes material, time, and personnel use, and allows selections for configurations of materials not included in the model by robust material attribute-based interpolation. The approach does not apply Principal Component Analysis (PCA) and Partial Least Squares (PLS). Instead, a reduced set of material attributes that require measurement was determined from existing material attribute libraries found in literature. Only those identified 17 attributes were measured for 13 materials and employed in this study. The established model proved to be predictive for an even further reduced attribute set of 12 attributes. The model introduces a simplified method for selecting feeding equipment combining precision metrics (relative mean intra-bin variability) and model parameters (fmax, fmin, beta). The reduction of Material Attributes (MAs) in this short-cut method enables fast evaluation and enhances resource efficiency by incorporating only a few selected MAs into the model and therefore introduces a different line of thinking. It was also possible to compare feeding of different hopper geometries, showing that flat bottom-hoppers have higher dosing precision than round bottom-hoppers. In conclusion, this work introduces a smart and innovative model that simplifies the equipment selection process and brings objectivity through a comprehensive numerical description of the discharge characteristics, and provides new options for continuous dosing in pharma through combining precision and curve fitting parameters.

Response of host Indian Musa germplasm against artificially induced root lesion nematodes (Pratylenchus coffeae)

Under greenhouse condition, the host response of fifty-nine banana genotypes (Musa spp.) from the Eumusa section to the root-lesion nematode (Pratylenchus coffeae) was assessed. In a factorial completely randomized design (FCRD) with five replications, healthy banana suckers of the diploid and triploid accessions were planted in cement pots. Uninoculated controls were included in the experiment as comparison to study the sensitivity of the genotypes. Two varieties (Pisang Lilin and Nendran) with known reaction to lesion nematode were also included as reference clones because of their resistance and susceptibility to P. coffeae. Banana accessions maintained in the pots were inoculated with infective juveniles of root-lesion nematode, P. coffeae at 45 days after planting at the rate of 400 nematodes/pot. Ninety days after inoculation, the plants were harvested to observe the response of the different banana genotypes to P. coffeae. Data were recorded on plant growth (plant height, girth at the base, number of standing leaves, number of roots and weight of the root system), root damage assessment (percentage of infected roots and percentage necrosis) and nematode reproduction. When inoculated with nematodes, the reduction in plant growth attributes was relatively higher in genotypes that were susceptible. The reduction in growth characters viz., plants height, number of roots and root fresh weight were maximum in the diploid genotypes Manguthamng (26.80, 27.2, 33.0 %) and Manohar (25.70, 29.2, 35.2 %) and in the triploid genotypes Kaali, Rajthali, Digjowa, Saapkal, Cheenichampa, Dasaman, Borchampa, Jahaji, Manjahaji, Barjahaji and Sabri when inoculated with Pratylenchus coffeae. Even after nematode inoculation, root investigations showed that resistant and tolerant genotypes had greater numbers of roots as well as high fresh and dry weight of roots. Root and soil population of nematodes assessed at 90 days after inoculation indicated very high population buildup of Pratylenchus coffeae (>28) in diploids Manguthamng and Nendrapadathi and in triploids Jahaji, Manjahaji, Saapkal, Borchampa, Therahaw-1163 and 1164, Ankur-I and Bersain. The rate was the lowest (<9) in the diploid genotypes Kanai Bansi, Kechulepa, M.balbisiana, Athiakol, Bhimkol and Aittakola and in the triploids Karthobiumtham and Ankur-II. The results of the experiment showed that the diploids Musa balbisiana (BB), Aittakola (BB), Bhimkol (BB), Kechulepa (BB), Kanai Bansi (AA) and Athiakol (BB) and the triploid genotypes Kachkel (ABB), Karthobiumtham (ABB) and Ankur-II (ABB) were resistant to Pratylenchus coffeae.

Attribute reduction based on directional semi-neighborhood rough set

Attribute reduction based on directional semi-neighborhood rough set

DCE attribute development investigating public policy for the provision of medicinal cannabis

Background Defining attributes and attribute levels for a discrete choice experiment (DCE) poses a significant challenge for practitioners exploring preferences for new or unfamiliar products due to the dearth of available information and limits in stakeholder knowledge. This study outlines a comprehensive process for identifying attributes and levels in a DCE aimed at gauging public preferences for health policies related to medicinal cannabis (MC). Methods A rigorous four-stage attribute development process was utilized and included i) the formulation of a preliminary attribute list from a scoping review and document analysis, ii) reduction of attributes via focus groups, iii) removal of inappropriate attributes using Delphi studies and research team knowledge, and iv) refinement of attribute language based on the feedback from think-aloud interviews. Results A base attribute list formed from the scoping review and document analysis served as effective discussion stimuli in focus groups, especially for participants with limited subject knowledge. Structured focus group activities proved more effective than open-ended discussions in engaging naive participants. Delphi studies were found to be overcomplex for expert-led attribute prioritization. Think-aloud interviews during a pilot DCE were essential for assessing attribute language and clarity and understanding participants’ decision-making processes. Conclusion The development of DCE attributes measuring preferences for an unfamiliar health product requires a multi-method approach. Evaluating the outcomes from various methods of attribute development yielded a refined list of attributes that were significant to stakeholders, allowed for meaningful trade-offs, and were presented in language accessible to the target population.

Transcriptome analysis of the effect of HERV-K env gene knockout in ovarian cancer cell lines.

Human endogenous retroviruses (HERVs) have been implicated in the pathogenesis of various diseases, particularly cancers. Previous investigations from our group demonstrated that targeted knockout (KO) of the HERV-K env gene led to a significant reduction in tumorigenic attributes, including proliferation, migration, and invasion of ovarian cancer cells. In this study, we aimed to elucidate the impact of HERV-K env KO on gene expression in ovarian cancer cell lines through comparative RNA sequencing (RNA-Seq) analysis with two distinct HERV-K env KO ovarian cancer cell lines, SKOV3 and OVCAR3. HERV-K env gene KO was achieved in SKOV3 and OVCAR3 ovarian cancer cell lines using the CRISPR-Cas9 system. Next-generation mRNA sequencing was employed to assess the gene expression profiles of both mock and HERV-K env KO ovarian cancer cells. Furthermore, comprehensive analyses involving gene ontology and pathway assessments were conducted. Transcriptome analysis revealed that 23 differentially expressed genes (DEGs) were upregulated and 17 DEGs were downregulated in SKOV3 cells. In OVCAR3 cells, 198 DEGs were upregulated, and 17 DEGs were downregulated. Notably, 53 DEGs exhibited statistically significant differences among the 1,612 DEGs identified. Our findings indicate that HERV-K env gene KO exerts a profound influence on gene expression patterns in OVCAR3 cells, while genetic alterations in expression were relatively modest in SKOV3 cells. Nevertheless, genes ND1, ND2, and CYTB displayed a common increase in expression, while ERRFI1 and NDRG1 exhibited a decrease in expression in both cell lines. Our study demonstrates that KO of the HERV-K env gene in ovarian cancer cell lines has a substantial impact on gene expression patterns and can be used to identify potential therapeutic targets for ovarian cancer and related diseases.

Rapid and optimized parallel attribute reduction based on neighborhood rough sets and MapReduce

Attribute reduction is a crucial step in data pre-processing and feature engineering. It is the selection of a subset of relevant data attributes to reduce the computational complexity of machine learning models and improve their performance. Neighborhood rough set (NRS) theory provides a valuable framework for attribute reduction. It leverages neighborhood information to identify non-redundant and informative attributes for data analysis and machine learning tasks. Attribute subsets based on NRS theory are highly qualitative, producing effective prediction accuracies in Euclidean space. However, existing NRS-based solutions are resource-intensive because of the large search space required for finding neighborhoods and redundant computations. To overcome these limitations, we propose the rapid and optimized attribute reduction (ROAR) algorithm that optimizes the current state-of-the-art attribute-reduction method in NRS theory. The strength of ROAR lies in its ability to accelerate computations by rapidly determining the neighborhood consistency of data samples and consequently expediting the identification of both positive and boundary regions. This efficiency significantly enhances the overall processing time for the data analysis tasks. Experimental results on 12 standard datasets demonstrate that the ROAR algorithm exhibits high efficiency by obtaining accurate reduction results with rapid response times. To ensure that the ROAR algorithm is suitable for high-dimensional datasets, we provide a parallel implementation, namely, the P-ROAR algorithm. The P-ROAR algorithm is the first parallel attribute-reduction algorithm in the classical NRS theory. Computational speeds and scalability metrics establish that P-ROAR is much faster and more scalable for datasets with an enormous attribute space. These algorithms provide a tool for handling feature reduction in data engineering without compromising accuracy and performance.

Attribute reduction using self-information uncertainty measures in optimistic neighborhood extreme-granulation rough set

The pessimistic and optimistic neighborhood multi-granulation rough sets (PNMRS and ONMRS) have been applied to attribute reduction. Nonetheless, the setting of the granulation space in both models is usually based on expert experience or continuous trials, which are poorly interpretable. Besides, the dependency, an important attribute evaluation function in PNMRS and ONMRS, only considers classification information in the lower approximation and ignores that of the upper approximation, affecting its evaluation effect. Thus, we firstly propose the pessimistic and optimistic neighborhood extreme-granulation rough sets (PNERS and ONERS) based on the minimum granulation space, avoiding the unobjective setting of the granulation space. However, PNERS describes knowledge with low accuracy, and therefore we construct two self-information uncertainty measures in ONERS and propose the joint self-information that is more suitable for attribute reduction as it takes full consideration of classification information in the lower and upper approximations. Then, an attribute reduction method using the joint self-information is designed, and Fisher Score is introduced into our method to decrease time cost when processing high dimensional data. Extensive experiments validate that our method is able to achieve superior reduction and classification results on different dimensional datasets.

Research and application of rough set attribute reduction algorithm based on genetic algorithm in pipeline network

Abstract Explosion accidents often occur in urban underground pipeline network systems, which has caused safety issues in public life and industrial production. GIS technology can visually manage pipeline equipment and solve the problem of valve closure analysis in the event of current accidents, avoiding the escalation of the situation, but it cannot make predictions. Attribute reduction is one of the core contents of rough set theory and is an NP-hard problem. This article proposes a custom attribute reduction algorithm based on an algorithm and uses the algorithm in the historical burst pipeline dataset of the water supply pipeline industry. The results have shown that this method combined with a decision tree can make predictions before water pipe leakage. The algorithm provides the best result of attribute reduction for decision tree methods, which will improve the speed and accuracy of decision-making.

BiFuG2-Spark: Bi-Directional Fuzzy Granular-Cabin Parallel Attribute Reduction Accelerator With Granular-Group Collaboration

BiFuG2-Spark: Bi-Directional Fuzzy Granular-Cabin Parallel Attribute Reduction Accelerator With Granular-Group Collaboration

Attribute Reduction Based on Fuzzy Distinguishable Pair Metric Considering Redundancy Upper and Lower Bounds

Attribute Reduction Based on Fuzzy Distinguishable Pair Metric Considering Redundancy Upper and Lower Bounds

Quantifying assumptions underlying peak oxygen consumption equations across the body mass spectrum.

The goal of this study is toquantify the assumptions associated with the Wasserman-Hansen (WH) and Fitness Registry and the Importance of Exercise: A National Database (FRIEND) predictive peak oxygen consumption (pVO2) equations across body mass index (BMI). Assumptions in pVO2 for both equations were first determined using a simulation and then evaluated using exercise data from the Stanford Exercise Testing registry. We calculated percent-predicted VO2 (ppVO2) values for both equations and compared them using the Bland-Altman method. Assumptions associated with pVO2 across BMI categories were quantified by comparing the slopes of age-adjusted VO2 ratios (pVO2/pre-exercise VO2) and ppVO2 values for different BMI categories. The simulation revealed lower predicted fitness among adults with obesity using the FRIEND equation compared to the WH equations. In the clinical cohort, we evaluated 2471 patients (56.9% male, 22% with BMI >30 kg/m2, pVO2 26.8 mlO2/kg/min). The Bland-Altman plot revealed an average relative difference of -1.7% (95% CI: -2.1 to -1.2%) between WH and FRIEND ppVO2 values with greater differences among those with obesity. Analysis of the VO2 ratio to ppVO2 slopes across the BMI spectrum confirmed the assumption of lower fitness in those with obesity, and this trend was more pronounced using the FRIEND equation. Peak VO2 estimations between the WH and FRIEND equations differed significantly among individuals with obesity. The FRIEND equation resulted in a greater attributable reduction in pVO2 associated with obesity relative to the WH equations. The outlined relationships between BMI and predicted VO2 may better inform the clinical interpretation of ppVO2 values during cardiopulmonary exercise test evaluations.

Attribute reduction with fuzzy divergence-based weighted neighborhood rough sets

Neighborhood rough sets are well-known as an interesting approach for attribute reduction in numerical/continuous data tables. Nevertheless, in most existing neighborhood rough set models, all attributes are assigned the same weights. This may undermine the capacity to select important attributes, especially for high-dimensional datasets. To establish attribute weights, in this study, we will utilize fuzzy divergence to evaluate the distinction between each attribute with the whole attributes in classifying the objects to the decision classes. Then, we construct a new model of fuzzy divergence-based weighted neighborhood rough sets, as well as propose an efficient attribute reduction algorithm. In our method, reducts are considered under the scenario of the α-certainty region, which is introduced as an extension of the positive region. Several related properties will show that attribute reduction based on the α-certainty region can significantly enhance the ability to identify optimal attributes due to reducing the influence of noisy information. To validate the effectiveness of the proposed algorithm, we conduct experiments on 12 benchmark datasets. The results demonstrate that our algorithm not only significantly reduces the number of attributes compared to the original data but also enhances classification accuracy. In comparison to some other state-of-the-art algorithms, the proposed algorithm also outperforms in terms of classification accuracy for almost all of datasets, while also maintaining a highly competitive reduct size and computation time.

A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis.

We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis.

Fuzzy object-induced network three-way concept lattice and its attribute reduction

Concept cognition and knowledge discovery under network data combine formal concept analysis with complex network analysis. However, in real life, network data is uncertain due to some limitations. Fuzzy sets are a powerful tool to deal with uncertainty and imprecision. Therefore, this paper focuses on concept-cognitive learning in fuzzy network formal contexts. Fuzzy object-induced network three-way concept (network OEF-concept) lattices and their properties are mainly investigated. In addition, three fuzzy network weaken-concepts are proposed. As the real data is too large, attribute reduction can simplify concept-cognitive learning by removing redundant attributes. Thus, the paper proposes attribute reduction methods that can keep the concept lattice structure isomorphic and the set of extents of granular concepts unchanged. Finally, an example is given to show the attribute reduction process of a fuzzy network three-way concept lattice. Attribute reduction experiments are conducted on nine datasets, and the results prove the feasibility of attribute reduction.

A Novel generalization of sequential decision-theoretic rough set model and its application

This paper introduces a refined and broadened version of decision-theoretic rough sets (DTRSs) named Generalized Sequential Decision-Theoretic Rough Set (GSeq-DTRS), which integrates the three-way decision (3WD) methodology. Operating through multiple levels, this iterative approach aims to comprehensively explore the boundary region. It introduces the concept of generalized granulation, departing from conventional equivalence-relation-based structures to incorporate similarity/tolerance relations. GSeq-DTRS addresses the limitations encountered by its predecessor, Seq-DTRS, particularly in managing sequential procedures and integrating new attributes. A notable advancement is its seamless handling of continuous-scale datasets through a defined Generalized Granular Structure (GGS), enabling classification across multiple levels without necessitating reduction of attributes. A refined version of conditional probability (CP), aligned with tolerance classes, enhances the approach, supported by a meticulously designed algorithm. Extensive experimental analysis conducted on two datasets sourced from https://www.kaggle.com demonstrates the efficacy of the procedure for both continuous and discrete datasets, effectively classifying probable elements into POS or NEG regions based on their membership. Unlike traditional Seq-DTRS, it does not require reduction of attributes at each new level. Additionally, the algorithm exhibits lower sensitivity to parametric values and yields results in fewer iterations. Thus, its potential impact on decision-making processes is readily apparent.

Efficient and Fast Algorithm for Attribute Reduction of Large Dimensional Data Using Rough Set Theory on Graphics Processing Unit

Efficient and Fast Algorithm for Attribute Reduction of Large Dimensional Data Using Rough Set Theory on Graphics Processing Unit

Attribute reduction for hierarchical classification based on improved fuzzy rough set

Attribute reduction plays a critical role in extracting valuable information from high-dimensional datasets. Compared to Pawlak rough set, fuzzy rough set can preserve more data information, making it a prominent focus in the research of attribute reduction. However, current fuzzy rough set-based attribute reduction focuses on flat classification, neglecting distinguishable stability information in hierarchical classification, which leads to insufficient data utilization and reduces the accuracy of attribute reduction. To address these issues, this paper presents two types of fuzzy rough set, named IFRS-I and IFRS-II. Especially, IFRS-I is an improved fuzzy rough set for flat classification, while IFRS-II is constructed based on IFRS-I for hierarchical classification. Unlike traditional fuzzy rough set, IFRS-II has the following two advantages: (1) a stability factor is designed to measure the stability difference among decision classes, (2) a tolerance index is designed to represent the tolerance distance between decision classes based on the approximate information of different decision classes in hierarchical classification. Finally, a stable and effective attribute reduction based on IFRS-II (ARIFRS-II) is designed for hierarchical classification. Experiments demonstrate that compared with the existing related algorithms, ARIFRS-II obtains a higher classification accuracy and stability, while maintaining a suitable number of subsets after reduction.

Attribute reduction based on intuitionistic fuzzy dominance mutual information in intuitionistic fuzzy information systems

Intuitionistic fuzzy information system (IFIS) is an extension of fuzzy information system that can represent more uncertain information and more accurately describe the essence of fuzziness. Attribute reduction is an important problem in processing and analyzing IFISs. The article tries to propose an attribute reduction method in view of intuitionistic fuzzy dominance mutual information in IFISs, whose information values are intuitionistic fuzzy numbers. First, an intuitionistic fuzzy dominance relation is established in IFISs according to intuitionistic fuzzy dominance degrees, and the intuitionistic fuzzy information structure generated by the intuitionistic fuzzy dominance relation is constructed. Then, the intuitionistic fuzzy dominance entropy and its variations are researched, and some of their properties are discussed. Subsequently, an attribute reduction method and its algorithm based on intuitionistic fuzzy dominance mutual information are given. Furthermore, numerical studies and statistical tests are presented to evaluate the performance of the proposed method. Theoretical research and experiments show that the raised attribute reduction method is applicable to IFISs.

Attribute reduction for heterogeneous data based on monotonic relative neighborhood granularity

The neighborhood rough set model serves as an important tool for handling attribute reduction tasks involving heterogeneous attributes. However, measuring the relationship between conditional attributes and decision in the neighborhood rough set model is a crucial issue. Most studies have utilized neighborhood information entropy to measure the relationship between attributes. When using neighborhood conditional information entropy to measure the relationships between the decision and conditional attributes, it lacks monotonicity, consequently affecting the rationality of the final attribute reduction subset. In this paper, we introduce the concept of neighborhood granularity and propose a new form of relative neighborhood granularity to measure the relationship between the decision and conditional attributes, which exhibits monotonicity. Moreover, our approach for measuring neighborhood granularity avoids the logarithmic function computation involved in neighborhood information entropy. Finally, we conduct comparative experiments on 12 datasets using two classifiers to compare the results of attribute reduction with six other attribute reduction algorithms. The comparison demonstrates the advantages of our measurement approach.

Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have numerous shortcomings, such as addressing complex constraints and conducting multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This novel method stands out by addressing complex constraints and facilitating multi-perspective effectiveness evaluations. It has several advantages: (1) it combines supervised and unsupervised learning methods, allowing for nuanced data interpretation and enhanced attribute selection; (2) by incorporating multi-granularity structures, the algorithm can analyze data at various levels of granularity. This allows for a more detailed understanding of data characteristics at each level, which can be crucial for complex datasets; and (3) by using neighborhood relations instead of indiscernibility relations, the method effectively handles uncertain and fuzzy data, making it suitable for real-world datasets that often contain imprecise or incomplete information. It not only selects the optimal granularity level or attribute set based on specific requirements, but also demonstrates its versatility and robustness through extensive experiments on 15 UCI datasets. Comparative analyses against six established attribute reduction algorithms confirms the superior reliability and consistency of our proposed method. This research not only enhances the understanding of attribute reduction mechanisms, but also sets a new benchmark for future explorations in the field.