Human Factor Analysis on Marine Accidents Based on Attribute Reduction

An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory

Attribute reduction is one of the most important research issues in the rough set theory. The purpose of attribute reduction is to find a minimal attribute subset that satisfies some specific criteria, while the minimal attribute subset is called attribute reduct. In this paper, we define a similarity-based attribute reduct based on a clustering perspective. Each decision class is treated as a cluster, and the defined similarity-based attribute reduct can maintain or increase the discriminating ability of different clusters in the case of removing redundant attributes. In view of this, firstly, we define the intra-class similarity for objects in the same decision class and the inter-class similarity for objects between different decision classes. Secondly, we define a similarity-based attribute reduct by maximizing intra-class similarity and minimizing inter-class similarity in the rough set model. Thirdly, by considering the heuristic search strategy, we also design a corresponding reduction method for the proposed attribute reduct. The experimental results indicate that compared with other representative attribute reducts, our proposed attribute reduct can significantly improve the classification performance.

An improved attribute reduction scheme with covering based rough sets

A common interest of all shipping industry stakeholders is safe and accident-free shipping. To reach that goal, one of the most important actions that can be done is to analyze previous marine accidents. It means finding causes of accidents and, based on the analysis results, implementing effective corrective measures that can help reduce such undesired events in the future and improve safety efforts in shipping. Since it is widely accepted that human error accounts for 80–85% of all marine accidents, the research was focused on the human factor analysis in marine accidents. In this paper, 135 marine accident reports recorded in the UK Marine Accident Investigation Branch (MAIB) database from 2010 to 2019 were analyzed. The analysis aimed to categorize causal factors and discover the ones that are the most common. The Human Factor Analysis and Classification System for Maritime Accidents (HFACS-MA) method was used to be able to do so. Furthermore, multiple linear regression was used to determine the relationship between the number of accidents and the most common HFACS-MA causal factors. The research revealed that the causes of marine accidents are primarily dependent on two human factor categories and confirmed that by influencing those human factors categories, the number of marine accidents could be reduced and shipping safety improved in general.

In the face of the explosive growth in data volume, current technologies have encountered many difficulties in both storage and knowledge discovery processes. Moreover, the quality of data has also deteriorated due to excessive noisy information, which reduces the effectiveness of machine learning models. Therefore, many solutions have been proposed, in which attribute reduction has emerged as an important research direction. Currently, research on attribute reduction has become very active and is primarily focused on the processing of decision tables. In this area, research on attribute reduction based on rough set theory and its extensions is considered a promising direction, which has been yielding many impressive results. To gain a clearer understanding of the attribute reduction research direction, this study will provide an overview of the methods of attribute reduction from their inception to the methods proposed in recent times.

Rough set theory is emerging as a powerful toll for reasoning about data. Attribute reduction is one of important topics in the research on the rough set theory. Heuristic, discernibility matrix and matrix method are three usually methods for designing attribute reduction algorithm in attribute reduction based on rough set. Some researchers use matrix method to design attribute reduction algorithm. The time complexity of the algorithm is O(|C| <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> |U| <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), and the computation of the algorithm is time consuming. To lower the time complexity, it was first proposed a new matrix, and provided an attribute reduction definition based on the new matrix. Then it was proved that the new attribute reduction definition is the same as the old reduction. At last, we used the matrix of attribute to define the significance of attribute, and designed a new attribute reduction algorithm, which time complexity is cut to O(|C||U|)+O(|C| <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> |U/C| <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). At the same time, an example was used to illustrate the new algorithm.

In this paper, we study the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the rectifier. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center parameters in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from rectifier of a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of rough fuzzy control by simulation in the paper. The focus of our experiments is on the constant current control in rectifier side of HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the only fuzzy control scheme in the rectifier side of HVDC system.

Attribute reduction plays an important role in pattern recognition and machine learning, and the theory of rough sets has become a commonly used model for attribute reduction since its superiority in describing and quantifying vagueness and uncertainty. However, little attention has been paid to analyzing the importance of core attributes in different circumstances, and core attributes are generally all selected into the reduct without considering their differences. In this study, the role of core attributes is analyzed in terms of their impacts on classification ability, and the three-way partition of attributes is proposed to distinguish different core attributes and condition attributes. Then, a unified approximate attribute reduction framework based on the three-way decision is introduced to keep the quality and resulting classification performance of the reduct. Moreover, a general forward-adding and back-deleting heuristic algorithm is developed to effectively select important attributes and also eliminate unimportant core and condition attributes in the boundary region. Comprehensive comparative experiments and statistical significance analysis are conducted on UCI data sets. The experimental results show that our method achieves a better attribute reduction rate and classification performance and also verify that core attributes are not always indispensable.

Tri-level attribute reduction in rough set theory

Attribute reduction plays an important role in rough set theory. It is an important application in data mining. In this paper, we focus on discussing the relation between set covering and attribute reduction in rough set theory. Based on the equivalence between minimal set covering and minimal attribute reduction sets, attribute reduction graph (ARG) is constructed. A novel algorithm to find the minimal attribute reduction sets, which is based on combination of binary discernibility matrix and graph theory is proposed in this paper. This algorithm demonstrates its efficiency and feasibility by an example.

Rough sets provide a useful tool for data preprocessing during data mining. However, many algorithms related to some problems in rough sets, such as attribute reduction, are greedy ones. Matroids propose a good platform for greedy algorithms. Therefore, it is important to study the combination between rough sets and matroids. In this paper, we investigate rough sets and matroids through their operators, and provide a matroidal method for attribute reduction in information systems. Firstly, we generalize four operators of rough sets to four operators of matroids through the interior, closure, exterior and boundary axioms, respectively. Thus, there are four matroids induced by these four operators of rough sets. Then, we find that these four matroids are the same one, which implies the relationship about operators between rough sets and matroids. Secondly, a relationship about operations between matroids and rough sets is presented according to the induced matroid. Finally, the girth function of matroids is used to compute attribute reduction in information systems.

Credit becomes an import issue for community and enterprise development. The development of internet makes available of enterprise credit data, which makes it possible to research enterprise credit evaluation. In this work, we propose an attribute reduction algorithm based on semantic analysis, rough set (RS) theory and conditional entropy. The collection of topic-related words is produced on the basis of semantic analysis and it is considered as the conditional attribute set. Then attribute reduction is actualised based on RS theory and conditional entropy. The method is applied to enterprise and community credit data which is available from the web. Extensive experiments on real-word data show that our method can obtain optimal attribute efficiently.

Despite the modernization and automation in marine technology and the implementation of safety-related regulations, marine accidents are still a main concern for global maritime transportation. It is well known that human error is the key contributor, but human error data is hard to be collected and classified and the factors which influence human behavior should be identified. For this reason, the goal of this paper constructed a case-based HFACS (Human Factor Analysis and Classification System) method for marine human factor data collection and classification, and the data collected produced statics for analysis and safety management.

Decision-theoretic rough sets (DTRS) can be seen as a kind of misclassification cost-sensitive learning model. In DTRS, attribute reduction is the process of minimizing misclassification costs. However in parctice, data are not free, and there are test costs to obtain feature values of objects. Hence, the process of attribute reduction should help minimizing both of misclassification costs and test costs. In this paper, the minimal test cost attribute reduct (MTCAR) problem is defined in DTRS. The objective of attribute reduction is to minimize misclassification costs and test costs. A genetic algorithm (GA) is used to solve this problem. Experiments on UCI data sets are performed to validate the effectiveness of GA to solve MTCAR problem.KeywordsDecision-theoretic rough setsmisclassification coststest costsattribute reductcost-sensitive

It is well known that human error is the key contributor in most marine accidents. Nevertheless, human error is not the cause of accidents or mishaps in the new view on human errors; it is the deeper trouble of the MMEM (Man-Machine-Environment-Management) system. The key point for human error analysis is to find out the connection between human error and the features of system elements, that is the contexts of human error (Operating environment, Organization influence, etc). For this reason, this paper shows an approach for mining the human factors data which influence the occurrence of human error. This approach bases on the data collected on HFACS (Human Factor Analysis and Classification System) Model. It can be used for analysis of human factors and risk management of marine industry.