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Data Mining Practical Machine Learning Tools and Techniques

Statistical Analysis with Missing Data

A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.

High-resolution mapping of global surface water and its long-term changes

Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, variable, etc. Hesitant fuzzy sets suit the modeling of quantitative settings; however, similar situations may occur in qualitative settings so that experts think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. In this paper, the concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms. Then, a multicriteria linguistic decision-making model is presented in which experts provide their assessments by eliciting linguistic expressions. This decision model manages such linguistic expressions by means of its representation using hesitant fuzzy linguistic term sets.

/pdf/hesitant-fuzzy-linguistic-term-sets-for-decision-making-4y2hc7e6bo.pdf

Hesitant Fuzzy Linguistic Term Sets for Decision Making

Belief rule base (BRB) systems are an extension of traditional IF-THEN rule based systems and capable of capturing complicated nonlinear causal relationships between antecedent attributes and consequents. In a BRB system, various types of information with uncertainties can be represented using belief structures, and a belief rule is designed with belief degrees embedded in its possible consequents. For a set of inputs to antecedent attributes, inference in BRB is implemented using the evidential reasoning (ER) approach. In this paper, the inference mechanism of the ER algorithm is analyzed first and its patterns of monotonic inference and nonlinear approximation are revealed. For a practical BRB system, it is difficult to determine its parameters accurately by using only experts' subjective knowledge. Moreover, the appropriate adjustment of the parameters of a BRB system using available historical data can lead to significant improvement on its prediction performance. In this paper, a training data selection scheme and an adaptive training method are developed for updating BRB parameters. Finally, numerical studies on a multi-modal function and a practical pipeline leak detection problem are conducted to illustrate the functionality of BRB systems and validate the performance of the adaptive training technique.

https://personalpages.manchester.ac.uk/staff/jian-bo.yang/JB%20Yang%20Journal_Papers/ChenYangXuZhouTang_BRB_Training_ESWA.pdf

Inference analysis and adaptive training for belief rule based systems

In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis.

/pdf/online-updating-belief-rule-base-using-the-rimer-approach-3f07aql2a0.pdf

Online Updating Belief-Rule-Base Using the RIMER Approach

Widespread adoption of automated decision making by artificial intelligence (AI) is witnessed due to specular advances in computation power and improvements in optimization algorithms especially in machine learning (ML). Complex ML models provide good prediction accuracy; however, the opacity of ML models does not provide sufficient assurance for their adoption in the automation of lending decisions. This paper presents an explainable AI decision-support-system to automate the loan underwriting process by belief-rule-base (BRB). This system can accommodate human knowledge and can also learn from historical data by supervised learning. The hierarchical structure of BRB can accommodates factual and heuristic rules. The system can explain the chain of events leading to a decision for a loan application by the importance of an activated rule and the contribution of antecedent attributes in the rule. A business case study on automation of mortgage underwriting is demonstrated to show that the BRB system can provide a good trade-off between accuracy and explainability. The textual explanation produced by the activation of rules could be used as a reason for denial of a loan. The decision-making process for an application can be comprehended by the significance of rules in providing the decision and contribution of its antecedent attributes.

https://www.research.manchester.ac.uk/portal/files/155874311/eswa_swati_revised.pdf

An Explainable AI Decision-Support-System to Automate Loan Underwriting

This paper presents the nonlinear interval optimization (NIO) model to solve optimal power system dispatch (OPSD) with uncertain wind power integrated. In this model, not only the average of the dispatching objective, but its deviation are also taken into account. Therefore, the NIO model based on OPSD is formulated as a multi-objective optimization problem. An optimization algorithm, group search optimizer with multiple producers (GSOMP) is applied to obtain Pareto solutions, which show the tradeoff relationship between the average and deviation of the dispatching objective. Then, a decision-making method, the evidential reasoning (ER) approach, is applied to determine the final dispatch solution. Simulation results based on the modified IEEE 30-bus system prove the applicability and effectiveness of the NIO model to deal with the OPSD, considering the integration of the uncertain wind power.

Optimal Power System Dispatch With Wind Power Integrated Using Nonlinear Interval Optimization and Evidential Reasoning Approach

Rapid and accurate identification of consumer demands and systematic assessment of product quality are essential to success for new product development, in particular for fast moving consumer goods such as food and drink products. This paper reports an investigation into a belief rule-based (BRB) methodology for quality assessment, target setting and consumer preference prediction in retro-fit design of food and drink products. The BRB methodology can be used to represent the relationships between consumer preferences and product attributes, which are complicated and nonlinear. A BRB system can initially be established using expert knowledge and then optimally trained and validated using data generated from consumer or expert panel assessments or from tests and experiments. The established BRBs can then be used to predict the consumer acceptance of new products or set product target values in retro-fit design. The proposed BRB methodology is applied to the design of a lemonade drink product using real data provided by a sensory product manufacturer in the UK. The results show that the BRB methodology can be used to predict consumer preferences with high accuracy and to set optimal target values for product quality improvement.

Dong-Ling Xu

Papers

Inference analysis and adaptive training for belief rule based systems

Online Updating Belief-Rule-Base Using the RIMER Approach

An Explainable AI Decision-Support-System to Automate Loan Underwriting

Optimal Power System Dispatch With Wind Power Integrated Using Nonlinear Interval Optimization and Evidential Reasoning Approach

Belief rule-based methodology for mapping consumer preferences and setting product targets