Classifying Cinnamomums using rough sets classifier based on interval-discretization

Classification, which is the task of assigning objects to one of several predefined categories, is a pervasive problem that encompasses many diverse applications. Decision tree classifier, which is a simple yet widely used classification technique, employs training data to yield decision rules; moreover, it can create thresholds and then split the list of continuous attributes into descrete intervals for handling continuous attributes (Quinlan in Journal of Artificial Intelligence Research 4:77–90, 1996). Rough set theory (Pawlak in International Journal of Computer and Information Sciences 11:341–356, 1982; International Journal of Man-Machine Studies 20:469–483, 1984; Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht, 1991) has been applied to a wide variety of decision analysis problems for the extraction of rules from databases. This paper proposes a hybrid approach that takes advantage of combining decision tree and rough sets classifier and applies it to plant classification. The introduced approach starts with decision tree classifier (C4.5) as preprocessing technique to make interval-discretization, subsequently, and uses rough set method for extracting rules. The proposed approach aims at finding out classification rules via analyzing lamina attributes (leaf stalk, leaf width, leaf length, length/width ratio) of Cinnamomum, which are gathered and measured by plant specialists in the field of Taiwan. A comparison with the widely used algorithms (e.g., decision tree, multilayer perceptrons, naïve Bayes, and rough sets classifier) is carried out to show numerous advantages of the proposed approach. Finally, employing with test data in which species are unknown, results of classification are approved by consulting the relative plant specialists.     

Decision Support Methods for Finding Phenotype — Disorder Associations in the Bone Dysplasia Domain

A lack of mature domain knowledge and well established guidelines makes the medical diagnosis of skeletal dysplasias (a group of rare genetic disorders) a very complex process. Machine learning techniques can facilitate objective interpretation of medical observations for the purposes of decision support. However, building decision support models using such techniques is highly problematic in the context of rare genetic disorders, because it depends on access to mature domain knowledge. This paper describes an approach for developing a decision support model in medical domains that are underpinned by relatively sparse knowledge bases. We propose a solution that combines association rule mining with the Dempster-Shafer theory (DST) to compute probabilistic associations between sets of clinical features and disorders, which can then serve as support for medical decision making (e.g., diagnosis). We show, via experimental results, that our approach is able to provide meaningful outcomes even on small datasets with sparse distributions, in addition to outperforming other Machine Learning techniques and behaving slightly better than an initial diagnosis by a clinician.     

Data mining of the GAW14 simulated data using rough set theory and tree-based methods

Rough set theory and decision trees are data mining methods used for dealing with vagueness and uncertainty. They have been utilized to unearth hidden patterns in complicated datasets collected for industrial processes. The Genetic Analysis Workshop 14 simulated data were generated using a system that implemented multiple correlations among four consequential layers of genetic data (disease-related loci, endophenotypes, phenotypes, and one disease trait). When information of one layer was blocked and uncertainty was created in the correlations among these layers, the correlation between the first and last layers (susceptibility genes and the disease trait in this case), was not easily directly detected. In this study, we proposed a two-stage process that applied rough set theory and decision trees to identify genes susceptible to the disease trait. During the first stage, based on phenotypes of subjects and their parents, decision trees were built to predict trait values. Phenotypes retained in the decision trees were then advanced to the second stage, where rough set theory was applied to discover the minimal subsets of genes associated with the disease trait. For comparison, decision trees were also constructed to map susceptible genes during the second stage. Our results showed that the decision trees of the first stage had accuracy rates of about 99% in predicting the disease trait. The decision trees and rough set theory failed to identify the true disease-related loci.     

An Efficient Soft Set-Based Approach for Conflict Analysis

Conflict analysis has been used as an important tool in economic, business, governmental and political dispute, games, management negotiations, military operations and etc. There are many mathematical formal models have been proposed to handle conflict situations and one of the most popular is rough set theory. With the ability to handle vagueness from the conflict data set, rough set theory has been successfully used. However, computational time is still an issue when determining the certainty, coverage, and strength of conflict situations. In this paper, we present an alternative approach to handle conflict situations, based on some ideas using soft set theory. The novelty of the proposed approach is that, unlike in rough set theory that uses decision rules, it is based on the concept of co-occurrence of parameters in soft set theory. We illustrate the proposed approach by means of a tutorial example of voting analysis in conflict situations. Furthermore, we elaborate the proposed approach on real world dataset of political conflict in Indonesian Parliament. We show that, the proposed approach achieves lower computational time as compared to rough set theory of up to 3.9%.     

基于c-均值聚类的粗糙集神经网络在心脏病诊断中的应用

采用c-均值聚类法将决策表中的连续条件属性进行离散化,用粗糙集处理离散化后的决策表系统得到简化规则,然后将规则集输入BP神经网络进行训练,并对测试集进行预测.以此模型对一组有关心脏病诊断的数据进行处理,得到的预测判准率达85%,而单独使用粗糙集或BPNN进行预测,则判准率分别为76%和82%;若在粗糙集和BPNN联用模型中,对原始数据采用传统的等距离离散化和等频率离散化等离散化方法,预测判准率则分别只有53%和77%.     

Mapping indicator models: From intuitive problem structuring to quantified decision-making in sustainable forest management

Most commonly, sustainability indicator sets presented as lists do not take into account interactions among indicators in a systematic manner. Vice versa, existing environmental indicator systems do not provide a formalized approach for problem structuring and quantitative decision support. In this paper, techniques for considering indicator relationships are highlighted and a coupled approach between a qualitative and a quantitative method is analysed. Cognitive mapping (CM) is used for structuring indicators and three different causal maps are derived based on established sustainability concepts: (a) criteria and indicators (C&I hierarchy), (b) indicator network, and (c) Driving Force-Pressure-State-Impact-Response (DPSIR) system. These maps are transferred to the Analytic Network Process (ANP) to allow their application in multi-criteria decision analysis (MCDA).In an application example, Pan-European indicators for sustainable forest management (SFM) are utilized in an ANP-based assessment. The effects of the model structure on the overall evaluation result are demonstrated by means of three reporting periods on Austrian forestry.In a comparative analysis of CM and ANP it is tested whether their measures of indicator significance do correspond. Both centrality in CM and single limited priorities in ANP have been reported to identify key indicators that play an important role in networks. We found out that the correspondence between CM and ANP is the stronger the more rigidly cause-effect relationships are interpreted, which is the case for the DPSIR system of SFM indicators.It is demonstrated that using indicator sets without consideration of the indicator interactions will cause shortcomings for evaluation and assessment procedures in SFM. Given strict and consistent definition of causal indicator relationships, a coupled use of CM and ANP is recommendable for both enhancing the process of problem structuring as well as supporting preference-based evaluation of decision alternatives.     

Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries

Human capital is one of the critical resources for high-tech industries such as semiconductor manufacturing to maintain their competitive advantages, yet it is seldom addressed in literature. Owing to the changing nature of knowledge workers in high-tech industries, jobs cannot be easily delineated. Thus, conventional personnel selection approaches based on static job characteristics no longer suffice. Focusing on the needs in real settings, this study aims to develop a manufacturing intelligence framework that integrates the rough set theory, support vector machine, and decision tree to extract useful patterns and intelligence from huge human resource data and production data to enhance the decision quality of human resource management that include identifying high-potential talents who fit the company culture and allocating the job with functional nature that matches the characteristics of the talent. To assess the validity of this approach, empirical studies were conducted on the basis of real data collected from semiconductor companies for comparison. The results have shown the practical viability of this approach.     

A decision tree system for finding genes in DNA.

MORGAN is an integrated system for finding genes in vertebrate DNA sequences. MORGAN uses a variety of techniques to accomplish this task, the most distinctive of which is a decision tree classifier. The decision tree system is combined with new methods for identifying start codons, donor sites, and acceptor sites, and these are brought together in a frame-sensitive dynamic programming algorithm that finds the optimal segmentation of a DNA sequence into coding and noncoding regions (exons and introns). The optimal segmentation is dependent on a separate scoring function that takes a subsequence and assigns to it a score reflecting the probability that the sequence is an exon. The scoring functions in MORGAN are sets of decision trees that are combined to give a probability estimate. Experimental results on a database of 570 vertebrate DNA sequences show that MORGAN has excellent performance by many different measures. On a separate test set, it achieves an overall accuracy of 95 %, with a correlation coefficient of 0.78, and a sensitivity and specificity for coding bases of 83 % and 79%. In addition, MORGAN identifies 58% of coding exons exactly; i.e., both the beginning and end of the coding regions are predicted correctly. This paper describes the MORGAN system, including its decision tree routines and the algorithms for site recognition, and its performance on a benchmark database of vertebrate DNA.     

生态系统管理的多目标体系和方法

20世纪90年代以来,人们开始采用“生态系统管理”这种基于生态系统原理的综合方法管理自然资源和生态环境,促进人类与自然的和谐发展。然而,生态系统自身的复杂性、动态性及不确定性特点使得“生态系统管理”难以形成明确的定义和方法体系,系统的多重尺度和目标也增加了管理的难度。近20年兴起的系统工程主要针对大规模复杂系统进行研究,实现系统的总体优化。多目标决策和决策支持系统是系统工程的常用技术方法,生态系统管理是“基于目标”的管理模式。本文概述了生态系统管理的概念和要素,从构建管理体系的角度着重阐述了生态系统管理目标体系的结构、构建过程及目标间的相互关系,针对生态系统的复杂特性探讨了多目标决策方法和决策支持系统在生态系统管理中的应用,以期能够对形成具有普遍意义和实际操作性的生态系统管理方法体系起到借鉴作用。     

Multi-criteria Analysis for Technique Assessment:Case Study from Industrial Coating

Environmental policy is oriented toward integrated pollution prevention, taking into consideration all environmental media (air, water, land) and energy consumption. Therefore, methods for assessing environmentally relevant installations are needed which take economic, technical, and especially ecological criteria into account simultaneously. Mass and energy flow models are used for the representation of production processes and form the basis for the inventory phase in life-cycle assessment (LCA). For the interpretation of LCA results and the weighting of the aggregated impact assessment indicators, approaches of multicriterion analysis (MCA) have been proposed. These can analyze ecological aspects as well as economic and technical criteria. Recent developments in LCA focus on decision support for policy makers or decision boards. Appropriate support for investment decisions on environmentally relevant installations, however, is rare.
Based on a case study of the sector called surface coating, an MCA of environmentally relevant installations is described. With the help of a mass and energy flow management system, alternative scenarios, depicting the use of solvent-reduced materials and environmentally friendly techniques, are modeled for the job coater processes in case studies of coating of mobile phones and coating of polyvinyl chloride (PVC) parts destined for the automobile industry. The modeled scenarios are further analyzed by using a multicriterion decision support module. The application of the outranking approach PROMETHEE is illustrated. A further investigation of the derived ranking can be obtained through sensitivity analyses. Moreover, the results derived by PROMETHEE are compared with the outcomes of the multicriterion approaches multiattribute utility theory and analytical hierarchy process.     

Decision support for clinical laboratory capacity planning

The design of a decision support system for capacity planning in clinical laboratories is discussed. The DSS supports decisions concerning the following questions: how should the laboratory be divided into job shops (departments/sections), how should staff be assigned to workstations and how should samples be assigned to workstations for testing. The decision support system contains modules for supporting decisions at the overall laboratory level (concerning the division of the laboratory into job shops) and for supporting decisions at the job shop level (assignment of staff to workstations and sample scheduling). Experiments with these modules are described showing both the functionality and the validity.     

An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.     

Modelling the Valuesphere and the Ecosphere:

Methods for Life Cycle Impact Assessment have to cope with two critical aspects, the uncertainty in values and the (unknown) system behaviour. LCA methodology should cope explicitly with these subjective elements. A structured aggregation procedure is proposed that differentiates between the technosphere and the ecosphere and embeds them in the valuesphere. LCA thus becomes a decision support system that models and combines these three spheres. We introduce three structurally identical types of LCA, each based on one coherent but different set of values. These sets of values can be derived from the Cultural Theory and are labeled as ‘egalitarian’, ‘individualistic’, and ‘hierarchic’. Within Life Cycle Impact Assessment, a damage oriented assessment model is complemented with both a newly developed precautionary indicator designed to address unknown damage and an indicator for the manageability of environmental damages. The indicators for unknown damage and for manageability complete the set of indicators judged to be relevant by decision makers. The weights given to these indicators are also value-dependent. The framework proposed here answers the criticisms that present LCA methodology does not strictly enough separate subjective from objective elements and that it fails to accurately model environmental impacts.     

Audit and feedback and clinical practice guideline adherence: Making feedback actionable

Background

Effective interventions prepare patients for making values-sensitive health decisions by helping them become informed and clarifying their values for each of the options. However, patient decision support interventions have not been widely implemented and little is known about effective models for delivering them to patients. The purpose of this study was to describe call centre nurses' adoption of a decision support protocol into practice following exposure to an implementation intervention and to identify factors influencing sustainable nursing practice changes.

Methods

Exploratory case study at a Canadian province-wide call centre guided by the Ottawa Model of Research Use. Data sources included a survey of nurses who participated in an implementation intervention (n = 31), 2 focus groups with nurses, interviews with 4 administrators, and a document review.

Results

Twenty-five of 31 nurses responded to the survey measuring adoption of decision support in practice. Of the 25 nurses, 11 had used the decision support protocol in their practice within one month of the intervention. Twenty-two of the 25 intended to use it within the next three months. Although some nurses found it challenging to begin using the protocol, most nurses reported that it: a) helped them recognize callers needing decision support; b) changed their approach to handling these calls; and c) was a positive enhancement to their practice. Strategies identified to promote sustainability of practice changes included integration of the decision support protocol in the call centre database, streamlining the patient decision aids for easier use via telephone, clarifying the administrative direction for the call centre's program, creating a call length guideline specific for these calls, incorporating decision support training in the staff development plan, and informing the public of this enhanced service.

Conclusion

Although most nurses adopted the decision support protocol for coaching callers facing values-sensitive decisions, to sustain practice changes, interventions are required to manage barriers in the practice environment and integrate decision support into the organization's policies, resources, and routine activities.     

Modelling the valuesphere and the ecosphere: Integrating the decision makers’ perspectives into LCA

Methods for Life Cycle Impact Assessment have to cope with two critical aspects, the uncertainty in values and the (unknown) system behaviour. LCA methodology should cope explicitly with these subjective elements. A structured aggregation procedure is proposed that differentiates between the technosphere and the ecosphere and embeds them in the valuesphere. LCA thus becomes a decision support system that models and combines these three spheres. We introduce three structurally identical types of LCA, each based on one coherent but different set of values. These sets of values can be derived from the Cultural Theory and are labeled as ‘egalitarian’, ‘individualistic’, and ‘hierarchic’. Within Life Cycle Impact Assessment, a damage oriented assessment model is complemented with both a newly developed precautionary indicator designed to address unknown damage and an indicator for the manageability of environmental damages. The indicators for unknown damage and for manageability complete the set of indicators judged to be relevant by decision makers. The weights given to these indicators are also value-dependent. The framework proposed here answers the criticisms that present LCA methodology does not strictly enough separate subjective from objective elements and that it fails to accurately model environmental impacts.     

Modelling the Valuesphere and the Ecosphere: Integrating the Decision Makers' Perspectives into LCA

Methods for Life Cycle Impact Assessment have to cope with two critical aspects, the uncertainty in values and the (unknown) system behaviour. LCA methodology should cope explicitly with these subjective elements. A structured aggregation procedure is proposed that differentiates between the technosphere and the ecosphere and embeds them in the valuesphere. LCA thus becomes a decision support system that models and combines these three spheres. We introduce three structurally identical types of LCA, each based on one coherent but different set of values. These sets of values can be derived from the Cultural Theory and are labeled as ‘egalitarian’, ‘individualistic’, and ‘hierarchic’. Within Life Cycle Impact Assessment, a damage oriented assessment model is complemented with both a newly developed precautionary indicator designed to address unknown damage and an indicator for the manageability of environmental damages. The indicators for unknown damage and for manageability complete the set of indicators judged to be relevant by decision makers. The weights given to these indicators are also value-dependent. The framework proposed here answers the criticisms that present LCA methodology does not strictly enough separate subjective from objective elements and that it fails to accurately model environmental impacts.     

神经外科临床决策支持系统设计研究

临床决策支持系统(Clinical decision support system,CDSS)是利用决策支持系统相关理论和技术,为临床医师在诊疗过程中提供诊疗决策的支持系统。本文根据临床神经外科疾病特点,提出基于KD、DB、和MD的框架系统结构,并构建临床疾病知识库、病人信息数据库和决策模型库,为下一步实现NCDSS功能提供理论基础和方法指导。     

Results and prognosis after highly selective vagotomy in the light of the mathematical theory of approximate sets]

Information system for 122 patients with peptic ulcer, who underwent highly selective vagotomy within 5 and 13 years earlier has been analysed with the use of technique based on the approximate sets theory. Cause-effect relationship between data describing the analysed patients before surgery and remote result of therapy (expressed in a 4-score Visick's scale) have been sought. Using a technique of the approximate sets, information system has been described from 11 to 5 significant and necessary preoperative data most closely related with the result of therapy. Models of the typical representatives of results classes have been constructed with the use of a/m data. Then, a decision algorithm has been made. Such an algorithm represents cause-effect relationship between significant data and the result of treatment. Models and decision algorithm for the favourable results of the treatment (I and II class in Visick's scale) constituted a base for the verification of indications to the highly selective vagotomy and construction of data base for computer-assisted system of the treatment of peptic ulcers with the highly selective vagotomy.     

Natural decision theory: A general formalism for the analysis of evolved characteristics

Classical Decision Theory, a mature and highly developed theory of rational choice, can be applied within evolutionary biology to the question of what traits an organism ought “rationally” to adopt, given that it wants to maximize its fitness. In this way the powerful formalism of decision theory can be brought to bear on the problem of how to predict which characters will be favored by natural selection, or to explain why certain characters have been so favored.Under some circumstances the classical theory of decision can be applied as it stands to an evolutionary problem simply by substituting an appropriate measure of biological fitness for the decision-theoretic concept of “utility”. Under other circumstances, however, it is necessary to extend the classical rules of decision in certain new directions. The result is a family of decision calculi of which the classical is only one. The name “Natural Decision Theory” is proposed for this extended class of biologically relevant decision methods.The decision tree method of diagramming an evolutionary decision situation is illustrated for the classical and three non-classical decision criteria, and is suggested as a potential means of gaining new insights into evolutionary forces.