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problexity-An open-source Python library for supervised learning problem complexity assessment

NEUROCOMPUTING 2023年 521卷 126-136页

作者： Komorniczak, Joanna Ksieniewicz, Pawel Wroclaw Univ Sci & Technol Fac Informat & Commun Technol Dept Syst & Comp Networks Wybrze Wyspianskiego 27 PL-50370 Wroclaw Poland

The problem's complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning - becoming the basis for determining meta-attributes or multi-criteria optimization - allowing the evaluation of the training set resampling without needing to rebuild the recognition model. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 classification complexity measures and 12 regression complexity measures for the Python language - compatible with the scikit-learn programming interface - allowing for the implementation of research using them in the most popular programming environment of the machine learning community. (c) 2022 Elsevier B.V. All rights reserved.

关键词： problem complexity Classification Regression Python

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Breakthrough invention and problem complexity: Evidence from a quasi-experiment

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STRATEGIC MANAGEMENT JOURNAL 2022年第12期43卷 2510-2544页

作者： Zhang, Yuchen Yang, Wei Tulane Univ Freeman Sch Business 7 McAlister Dr New Orleans LA 70118 USA China Europe Int Business Sch CEIBS Dept Strategy & Entrepreneurship Shanghai Peoples R China

Research Summary problem formulation is central to recombinant invention, and problem complexity particularly shapes the process and outcome of knowledge recombination. However, research on the antecedents of problem complexity remains limited. This study examines how breakthrough inventions may serve as an important antecedent of the complexity of problems formulated by individual inventors. We propose that a breakthrough invention may facilitate inventors' appreciation of novel knowledge couplings and improve their overall comprehension of knowledge interdependence for problem formulation, thus increasing problem complexity. We further argue that inventors' prior search breadth and experimentation strengthen the above effect. By exploiting the unexpected victory of AlphaGo and tracking the questions posted on *** by developers interested in deep learning, we find empirical evidence that supports our hypotheses. Managerial Summary Because formulating complex problems may result in novel and valuable inventions, it is helpful to understand what drives inventors to formulate problems of higher complexity. In this study, we propose that breakthrough inventions can serve as an important antecedent of problem complexity. In the context of deep learning, we find that developers formulate problems of higher complexity after the unexpected victory of AlphaGo, a widely acknowledged inventive breakthrough. Further, the above effect is more pronounced when developers have broad search experience in the past or engage frequently in experimentation. The insights of this study are not only relevant to managerial practices within the dynamic field of deep learning but also generalizable to broader technological contexts with breakthrough inventions.

关键词： breakthrough invention deep learning knowledge recombination machine learning problem complexity problem formulation

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How people do relational reasoning? Role of problem complexity and domain familiarity

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COMPUTERS IN HUMAN BEHAVIOR 2014年 41卷 319-326页

作者： Kumar, Shikhar Cervesato, Iliano Gonzalez, Cleotilde Univ Arizona Sch Informat Sci Technol & Arts Tucson AZ 85721 USA Carnegie Mellon Univ Qatar Sch Comp Sci Doha Qatar Carnegie Mellon Univ Dept Social & Decis Sci Pittsburgh PA 15213 USA

The goal of this paper is to study how people do relational reasoning, such as selecting the grade of all students in a class with GPA (Grade Point Average) greater than 3.5. Literature in the field of psychology of human reasoning offer little insight as to how people solve relational problems. We present two studies that look at human performance in relational problems that use basic relational operators. Our results present the first evidence toward the role of problem complexity on performance as determined by the accuracy and discrimination rates. We also look at the role of familiarity with tabular representation of information, as found in spreadsheets for example, and other factors for relational reasoning, and show that familiarity does not play a significant role in determining performance in relational problem solving, which we found counterintuitive. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Relational reasoning problem solving Spreadsheet Domain familiarity problem complexity

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A Research on Measuring and Reducing problem complexity to Increase System Affordability: From Theory to Practice 1

A Research on Measuring and Reducing Problem Complexity to I...

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Conference on Systems Engineering Research

作者： Salado, Alejandro Nilchiani, Roshanak Stevens Inst Technol Hoboken NJ 07030 USA

Requirement engineering is the cornerstone of systems engineering. Numerous large scale engineered systems face schedule delays, cost overruns and performance shortfalls that can be traced back to the requirements they need to fulfill. In fact, previous research has demonstrated strong relationship between requirements and systems affordability. This paper summarizes and puts into context the authors' novel contributions in three domains of requirements engineering: systems theory, complexity science, and systems methodologies. The authors propose new theorems and their proofs on requirements affecting affordability, propose a new complexity metric at requirement stage that measures the complexity limit of the system at conceptual stage (even before a specific design is determined), and propose two methodologies to elicit excess-free requirement sets and to identify conflicting requirements more effectively. The paper showcases the value of structuring a research in such a manner, i.e. from theory to practice, enabling strengthening the bounds between theorists and practitioners. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： problem complexity systems theory systems engineering methods requirements engineering

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Human vs. AI: Exploring students' preferences between human and AI TA and the effect of social anxiety and problem complexity

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年第1期29卷 1217-1246页

作者： Peng, Ziqing Wan, Yan Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China

Understanding preferences surrounding artificial intelligence (AI) and human teaching assistants (TAs) helps managers improve AI TAs, effectively deploying AI and human TAs, and providing better services to learners. The literature has explored how AI TAs' characteristics affect students' use intention, neglecting students' comparative behaviors between AI and human TAs, and overlooking the influence of differences between AI and human TAs on student preferences. Based on preference theory, trust theory, and the stimulus-organism-response (SOR) framework, we constructed a mechanism model by which differences between AI and human TAs affect student preferences. We held 26 semi-structured interviews and collected 401 valid questionnaires to validate it. We also examined the influence and moderating effect of social anxiety and problem complexity on student preferences. Differences in response quality and communication ability impacted differences in ability trust;differences in service attitude and psychological safety influenced differences in benevolent trust;differences in response time impacted differences in integrity trust. In turn, differences in trust affected student preferences. Social anxiety positively impacted students' AI TA preferences and negatively moderated the effect of differences in ability trust on students' AI TA preferences. problem complexity negatively affected students' AI TA preferences and moderated the effects of benevolence and differences in integrity trust on students' AI TA preferences. We propose a theoretical model to clarify the effects of differences between AI and human TAs on student preferences and to identify boundary conditions. Our findings provide new insights into AI TA research and offer suggestions for AI TA developers, managers, human TAs, and learners.

关键词： Chatbot AI teaching assistant Usage preference Trust Social anxiety problem complexity

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A Research on Measuring and Reducing problem complexity to Increase System Affordability: From Theory to Practice

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Procedia Computer Science 2015年 44卷 21-30页

作者： Alejandro Salado Roshanak Nilchiani Stevens Institute of Technology Hoboken NJ 07030 NJ (USA)

Requirement engineering is the cornerstone of systems engineering. Numerous large scale engineered systems face schedule delays, cost overruns and performance shortfalls that can be traced back to the requirements they need to fulfill. In fact, previous research has demonstrated strong relationship between requirements and systems affordability. This paper summarizes and puts into context the authors’ novel contributions in three domains of requirements engineering: systems theory, complexity science, and systems methodologies. The authors propose new theorems and their proofs on requirements affecting affordability, propose a new complexity metric at requirement stage that measures the complexity limit of the system at conceptual stage (even before a specific design is determined), and propose two methodologies to elicit excess-free requirement sets and to identify conflicting requirements more effectively. The paper showcases the value of structuring a research in such a manner, i.e. from theory to practice, enabling strengthening the bounds between theorists and practitioners.

关键词： problem complexity systems theory systems engineering methods requirements engineering.

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Combinatorial complexity problem reduction by the use of artificial vaccines

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第5期40卷 1871-1879页

作者： Montiel, Oscar Javier Diaz-Delgadillo, Francisco Sepulveda, Roberto Inst Politecn Nacl CITEDL Tijuana 22510 BC Mexico

A general method to reduce computing time for large combinatorial optimization problems by the use of a novel proposal is presented. It is based on reducing the problem complexity by the systematic application of vaccines, it is inspired in the concept of immunization derived from Artificial Immune Systems. The method can be applied practically to any combinatorial problem program solver such as genetic algorithms, memetic algorithms, artificial immune systems, ant colony optimization, the Dantzig-Fulkerson-Johnson algorithm, etc., providing optimal and suboptimal routes outperforming the selected algorithm itself. As a direct consequence of reducing problem complexity, the method provides a means to bring combinatorial optimization open problems that are too big to be treated by known techniques to a tractable point where acceptable solutions can be obtained. To demonstrate the proposed methodology the Traveling Salesman problem for huge quantity of cities was used, we tested the method with modern evolutionary algorithms and the Concorde program. Comparative experiments that shows the effectiveness of the method are presented. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： ALS COP TSP problem complexity

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New complexity measures for the facility layout problem: an empirical study using traditional and neural network analysis

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COMPUTERS & INDUSTRIAL ENGINEERING 1999年第3期36卷 585-602页

作者： Rossin, DF Springer, MC Klein, BD Univ Michigan Sch Management Dearborn MI 48128 USA Western Washington State Univ Coll Business & Econ Dept Management Bellingham WA 98225 USA

The most frequent application of the quadratic assignment problem has been in facility layout, which is concerned with locating the activities or departments of an organization in a fixed set of locations such that those activities with the strongest interrelationships are closest to each other. Early attempts to explain the varying performance of optimal and heuristic solution procedures on different layout problems focused on a univariate complexity measure such as the coefficient of variation. Recently, a multivariate measure for assessing the complexity of the quadratic assignment problem (QAP) formulation of the facility layout problem has been proposed. This paper introduces a new thresholding procedure for capturing critical relationship information, expands the eight components of the multivariate complexity measure by adding two new components, demonstrates a new solution value bound and experimentally determines the effect on solution quality of using either one of two heuristic solution procedures versus an optimal procedure. This result is important because it enables layout planners to determine in advance the likelihood of obtaining an optimal solution. It also tells them which problem characteristics are important and their relative weights in determining the solution outcome. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： facility layout quadratic assignment problem problem complexity

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Multiple comparison procedures applied to model selection

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NEUROCOMPUTING 2002年第1-4期48卷 155-173页

作者： Pizarro, J Guerrero, E Galindo, PL Univ Cadiz Dept Lenguajes & Sistemas Informat Cadiz Spain Univ Cadiz Inteligencia Artificial Grupo Sistemas Inteligent Cadiz Spain

This paper presents a new approach to model selection based on hypothesis testing. We first describe a procedure to generate different scores for any candidate model from a single sample of training data and then discuss how to apply multiple comparison procedures (MCP) to model selection. MCP statistical tests allow us to compare three or more groups of data while controlling the probability of making at least one Type I error. The complete procedure is illustrated on several model selection tasks, including the determination of the number of hidden units for feed-forward neural networks and the number of kernels for RBF networks. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： model selection multiple comparison procedures generalization network size problem complexity

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Influence of model management systems on decision making: empirical evidence and implications

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2000年第8期51卷 936-948页

作者： Chung, QB Willemain, TR O'Keefe, RM Villanova Univ Villanova PA 19085 USA Rensselaer Polytech Inst Troy NY 12181 USA Brunel Univ Uxbridge UB8 3PH Middx England

Model management (MM) regards decision models as an important organisational resource deserving prudent management. Despite the remarkable volume of model management literature compiled over the past twenty-odd years, very little is known about how decision makers actually benefit from employing model management systems (MMS). In this paper, we report findings from an experiment designed to verify the idea that the adequacy of modeling support provided by a MMS influences the decision maker's problem solving performance and behaviour. We show that the decision makers who receive adequate modelling support from MMS outperform those without such support. Also, we provide empirical evidence that the MMS help turn the decision makers' perception of problem solving from a number crunching task into development of solution strategies, consequently changing their decision making behaviour.

关键词： model management systems decision support systems problem complexity solution strategy qualitative analysis decision making behaviour

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