This paper will present a novel concept of expected values of fuzzy variables, which is essentially a type of Choquet integral and coincides with that of random variables. In order to calculate the expected value of general fuzzy variable, a fuzzy simulation technique is also designed. Finally, we construct a spectrum of fuzzy expected value models, and integrate fuzzy simulation, neural network, and genetic algorithms to produce a hybrid intelligent algorithm for solving general fuzzy expected value models.

Expected value of fuzzy variable and fuzzy expected value models

This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.

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KEEL: a software tool to assess evolutionary algorithms for data mining problems

Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty Judging beauty and other aesthetic qualities of photographs is a highly subjective task Hence, there is no unanimously agreed standard for measuring aesthetic value In spite of the lack of firm rules, certain features in photographic images are believed, by many, to please humans more than certain others In this paper, we treat the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated online photo sharing Website as data source We extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images Automated classifiers are built using support vector machines and classification trees Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings The work attempts to explore the relationship between emotions which pictures arouse in people, and their low-level content Potential applications include content-based image retrieval and digital photography

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Studying aesthetics in photographic images using a computational approach

Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide “obviously” interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Methods of applying adaptive social computing systems are disclosed. The social computing systems include capabilities to generate adaptive recommendations and representations of social networks derived, at least in part, from inferences of the preferences and interests of system users based on a plurality of usage behaviors, spanning a plurality of usage behavior categories. The behavioral categories include system navigation behaviors, content referencing behaviors, collaborative behaviors, and the monitoring of physical location and changes in location. Privacy control functions and compensatory functions related to insincere usage behaviors can be applied. Adaptive recommendation delivery can take the form of visual-based or audio-based formats.

Adaptive social computing methods

With known systems, when performing analysis of data, users typically have a solution-oriented view: there is a problem, and data has been collected with a view to providing some information about the problem Usually a method is selected, based on vague knowledge about some feature of the problem This is often sub-optimal, because the user can be assumed to be an expert in the application area, but not in data analysis Embodiments of the invention provide a means of characterising data analysis methods in terms of tangible and non-tangible parameters such as user-friendliness, accuracy, simplicity, interpretability, speed, cost, comprehensibility, and transparency, and then matching these to some pre-specified requirements This means is provided by a method of selecting a data analysis method in accordance with a user preference, wherein the user preference relates to a feature of the data analysis method and is represented by a fuzzy set comprising a range of values The method comprises the following steps: (i) using the user preference to identify one or more rules corresponding to the user preference, each rule comprising at least one fuzzy set that relates features of data analysis methods to data analysis characteristics; (ii) evaluating the or each identified rule, thereby identifying an instance of a data analysis characteristic associated with the identified rule, the instance comprising a fuzzy set having a range of values; (iii) accessing a plurality of data analysis methods, each of which has a plurality of data analysis characteristics, wherein each of which data analysis characteristics is characterised by a fuzzy set having a range of values; and (iv) comparing the data analysis characteristics associated with the accessed data analysis methods with the data analysis characteristic instance in order to identify a data analysis method that matches the user preference

Method and apparatus for data analysis

Identifying customer segments and tracking their change over time is an important application for enterprises who need to understand what their customers expect from them - now and in the future. This in particular is important for businesses which operate in dynamic markets with customers who, driven by new innovations and competing products, have highly changing demands and attitudes. Customer segmentation is typically done by applying some form of cluster analysis to obtain a set of segments to which future customers are assigned to. In this paper, we present a system for customer segmentation which accounts for the dynamics of today's markets. It employs an approach based on the discovery of frequent itemsets and the analysis of their change over time which, finally, results in a change-based notion of segment interestingness. Our approach allows us to detect arbitrary segments and analyse their temporal development. Thereby, our approach is assumption-free and pro-active and can be run continuously. Newly discovered segments or relevant changes will be reported automatically based on the application of several interestingness measures.

Mining changing customer segments in dynamic markets

Methods and systems for analysing data from a monitoring system for monitoring characteristics of a dynamic system, said monitoring system providing characteristic data in respect of a dynamic system with at least one known normal state, the analysis system comprising: means (100) for receiving characteristic data from the monitoring system, means (101) for receiving confirmation information from an operator when the dynamic system is in a known normal state, normality modelling means (105) for deriving a normality model comprising data indicative of known normal states in response to received characteristic data and confirmation information, prediction generating means (105) for predicting future characteristic data from the normality model, difference function providing means (105) for providing a difference function indicating an acceptable difference between predicted and received characteristic data, and comparison means (105) for comparing predicted characteristic data with received characteristic data in conjunction with the difference function and producing an abnormality signal if the difference exceeds the difference function.

Data analysis system and method

The present invention relates to identifying patterns of change in system operating parameters which may be used to identify unexpected operational conditions and to trigger an appropriate alert or action. The present invention provides a method (300) for controlling a network (350) using operating data for the network, the method comprising: receiving the network operating data (110); dividing the received data into sequential time periods (305); determining a structural description such as an association rule for the received data in each time period (310); identifying a change pattern in the determined structural description over the time periods (315); controlling the network (325) using a predetermined action corresponding to a predetermined structural description change pattern in response to the identified structural description change pattern matching the predetermined structural description change pattern (320).

Data processing method for controlling a network

Detecting recent changepoints in time-series can be important for short-term prediction, as we can then base predictions just on the data since the changepoint. In many applications we have panel data, consisting of many related univariate time-series. We present a novel approach to detect sets of most recent changepoints in such panel data which aims to pool information across time-series, so that we preferentially infer a most recent change at the same time-point in multiple series. Our approach is computationally efficient as it involves analysing each time-series independently to obtain a profile likelihood like quantity that summarises the evidence for the series having either no change or a specific value for its most recent changepoint. We then post-process this output from each time-series to obtain a potentially small set of times for the most recent changepoints, and, for each time, the set of series which has their most recent changepoint at that time. We demonstrate the usefulness of this method on two data sets: forecasting events in a telecommunications network and inference about changes in the net asset ratio for a panel of US firms.

Martin Spott

Papers

Method and apparatus for data analysis

Mining changing customer segments in dynamic markets

Data analysis system and method

Data processing method for controlling a network

Most recent changepoint detection in Panel data