An artificial immune systems based predictive modelling approach for the multi-objective elicitation of Mamdani fuzzy rules: a special application to modelling alloys

In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability

Improving Transparency in Approximate Fuzzy Modeling Using Multi-objective Immune-Inspired Optimisation

In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, prediction accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in eliciting models which are as transparent as possible, a ‘tricky’ exercise in itself. The proposed mechanism adopts a multi-stage modeling procedure and a variable length coding scheme to account for the enlarged search space due to simultaneous optimisation of the rule-base structure and its associated parameters. We claim here that IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference scheme and the defuzzification method. The proposed modeling approach has been compared to other representatives using a benchmark problem, and was further applied to a high-dimensional problem, taken from the steel industry, which concerns the prediction of mechanical properties of hot rolled steels. Results confirm that IMOFM is capable of eliciting not only accurate but also transparent FRBSs from quantitative data

More on the Ethics of E-Discovery: Predictive Coding and Other Forms of Computer-Assisted Review

This paper was circulated at a TAR conference hosted by the Bolch Judicial Institute (then the Center for Judicial Studies) in 2015. With the author\u27s permission, the paper has been archived in the scholarship repository. This document does not represent the views of Duke Law School, Duke University, their faculties, or any other organization

The ISIS Twitter census: defining and describing the population of ISIS supporters on Twitter

Presents a demographic snapshot of ISIS supporters on Twitter by analysing a sample of 20,000 ISIS-supporting Twitter accounts, mapping the locations, preferred languages, and the number and type of followers of these accounts. Overview Although much ink has been spilled on ISIS’s activity on Twitter, very basic questions about the group’s social media strategy remain unanswered. In a new analysis paper, J.M. Berger and Jonathon Morgan answer fundamental questions about how many Twitter users support ISIS, who and where they are, and how they participate in its highly organized online activities. Previous analyses of ISIS’s Twitter reach have relied on limited segments of the overall ISIS social network. The small, cellular nature of that network—and the focus on particular subsets within the network such as foreign fighters—may create misleading conclusions. This information vacuum extends to discussions of how the West should respond to the group’s online campaigns. Berger and Morgan present a demographic snapshot of ISIS supporters on Twitter by analyzing a sample of 20,000 ISIS-supporting Twitter accounts. Using a sophisticated and innovative methodology, the authors map the locations, preferred languages, and the number and type of followers of these accounts. Among the key findings: From September through December 2014, the authors estimate that at least 46,000 Twitter accounts were used by ISIS supporters, although not all of them were active at the same time.  Typical ISIS supporters were located within the organization’s territories in Syria and Iraq, as well as in regions contested by ISIS. Hundreds of ISIS-supporting accounts sent tweets with location metadata embedded.  Almost one in five ISIS supporters selected English as their primary language when using Twitter. Three quarters selected Arabic. ISIS-supporting accounts had an average of about 1,000 followers each, considerably higher than an ordinary Twitter user. ISIS-supporting accounts were also considerably more active than non-supporting users. A minimum of 1,000 ISIS-supporting accounts were suspended by Twitter between September and December 2014. Accounts that tweeted most often and had the most followers were most likely to be suspended. Much of ISIS’s social media success can be attributed to a relatively small group of hyperactive users, numbering between 500 and 2,000 accounts, which tweet in concentrated bursts of high volume. Based on their key findings, the authors recommend social media companies and the U.S government work together to devise appropriate responses to extremism on social media. Approaches to the problem of extremist use of social media, Berger and Morgan contend, are most likely to succeed when they are mainstreamed into wider dialogues among the broad range of community, private, and public stakeholders