Multivariate-Stepwise Gaussian Classifier (MSGC): A New Classification Algorithm Tested Over Real Disease Data Sets

In data mining, classification is the process of assigning one amongst previously known classes to a new observation. Mathematical algorithms are intensively used for classification. In these, a generalization is inferred from the data, so as to classify new cases, or individuals. The algorithm may misclassify an individual if the inference machine is not able to sufficiently discriminate it. Therefore, it is necessary to go further into the analysis of the information provided by the individual, until it can be sufficiently identified as belonging to a class. This chapter developed this idea for the improvement of a certain class of classifiers, using medical data sets to validate the new algorithm proposed here: The Multivariate-Stepwise Gaussian Classifier (MSGC). The results showed that MSGC is at least as competitive as the Gaussian Maximum Likelihood Classifier. MSGC attained the greatest accuracy rate in two of the data sets, and obtained identical results in the two remaining data sets. Concerning medical applications, once a classification method has been successfully validated considering a particular scope of data, the recommendable would be its use for the best diagnosis. Meanwhile, other algorithms could be tested until they proved to be effective enough to be put into practice

Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the USN Operating Forces

NPS NRP Technical ReportThe SECNAV disperses Navy forces in a deliberate manner to support DoD guidance, policy and budget. The current SLD process is labor intensive, takes too long, and needs AI. The research questions are: - How does the Navy weight competing demands for naval forces between the CCMDs to determine an optimal dispersal of operating forces? - How does the Navy optimize force laydown to maximize force development (Fd) and force generation (Fg) efficiency? We propose LAILOW to address the questions. LAILOW was derived from the ONR funded project and focuses on deep analytics of machine learning, optimization, and wargame. Learn: When there are data, data mining, machine learning, and predictive algorithms are used to analyze data. Historical Phased Force Deployment Data (TPFDDs) and SLD Report Cards data among others, one can learn patterns of what decisions were made and how they are executed with in the past. Optimize: Patterns from learn are used to optimize future SLD plans. A SLD plan may include how many homeports, home bases, hubs, and shore posture locations (Fd) and staffs (Fg). The optimization can be overwhelming. LAILOW uses integrated Soar reinforcement learning (Soar-RL) and coevolutionary algorithms. Soar-RL maps a total SLD plan to individual ones used in excursion modeling and what if analysis. Wargame: There might be no or rare data for new warfighting requirements and capabilities. This motivates wargame simulations. A SLD plan can include state variables or problems (e.g., future global and theater posture, threat characteristics), which is only observed, sensed, and cannot be changed. Control variables are solutions (e.g., a SLD plan). LAILOW sets up a wargame between state and control variables. Problems and solutions coevolve based on evolutionary principles of selection, mutation, and crossover.N3/N5 - Plans & StrategyThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions

Co-evolution and networks adaptation.

What is the role of co-evolution in the adaptation of a population of firms to a hostile environment ? To answer this question, we revisit network sociology starting from Kauffman s biological computer model. We apply a qualitative methodology to update exploitation and exploration mechanisms in nine Japanese interfirm networks. From these results, this article draws a typology of the adaptation forms, distinguishing pack, migratory, herd and colony networks.Sociologie des organisations; Réseaux d’entreprises;

A meta-architecture analysis for a coevolved system-of-systems

Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments. Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity that engineered systems-of-systems exhibit poses challenges to traditional systems engineering approaches. Systems engineers are presented with the problem of understanding how these systems can be designed or adapted given these challenges. Understanding how the environment influences system-of-systems performance allows systems engineers to target the right set of capabilities when adapting the system for improved performance. This research explores coevolution in a counter-trafficking system-of-systems and develops an approach to demonstrate its impacts. The approach implements a trade study using swing weights to demonstrate the influence of coevolution on stakeholder value, develops a novel future architecture to address degraded capabilities, and demonstrates the impact of the environment on system performance using simulation. The results provide systems engineers with a way to assess the impacts of coevolution on the system-of-systems, identify those capabilities most affected, and explore alternative meta-architectures to improve system-of-systems performance in new environments --Abstract, page iii

Organizational Adaptation

Organizational adaptation is equivocal. On the one hand, the concept is ubiquitous in management research and acts as the glue binding together the central issues of organizational change, performance, and survival. On the other hand, it lurks around in various guises (e.g., “fit,” “alignment,” “congruence,” and “strategic change”) studied from multiple theoretical streams (e.g., behavioral, resource based, and institutional) and at different levels of analysis (e.g., organization and industry levels). In a novel approach to reviewing 443 adaptation articles that leverages both computational and hand-coded analysis, we produce an interactive visual of the themes most studied by adaptation scholars. We inductively draw out a definition of adaptation as intentional decision making undertaken by organizational members, leading to observable actions that aim to reduce the distance between an organization and its economic and institutional environments. We then review the literature across three main areas of inquiry and six theoretical perspectives that surfaced from our analysis and identify 11 difficulties that have hampered adaptation research in the past 50 years. Our review suggests ways to address these difficulties to enable future research to develop and cumulate