Algorithms for finding attribute value group for binary segmentation of categorical databases

科研費報告書収録論文(課題番号:13680387・基盤研究(C)(2)・H13～H15/研究代表者:徳山, 豪/パラメトリック最適化を用いた幾何学データ処理の研究

Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms

The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorithmic implications of using this measure for optimization. We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods. We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one. Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.Comment: Accepted to Proceedings of the IEEE International Conference on Data Mining (ICDM'18

Comparative between optimization feature selection by using classifiers algorithms on spam email

Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of classifiers using K-nearest Neighbor (KNN), Naïve Bayesian (NB), and Support Vector Machine (SVM) on spam classifiers (without using feature selection) also enhances the reliability of feature selection by proposing optimization feature selection to reduce number of features that are not important

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems

Towards a unified method to synthesising scenarios and solvers in combinatorial optimisation via graph-based approaches

Hyper-heuristics is a collection of search methods for selecting, combining and generating heuristics used to solve combinatorial optimisation problems. The primary objective of hyper-heuristics research is to develop more generally applicable search procedures that can be easily applied to a wide variety of problems. However, current hyper-heuristic architectures assume the existence of a domain barrier that does not allow low-level heuristics or operators to be applied outside their designed problem domain. Additionally the representation used to encode solvers differs from the one used to encode solutions. This means that hyper-heuristic internal components can not be optimised by the system itself. In this thesis we address these issues by using graph reformulations of selected problems and search in the space of operators by using Grammatical Evolution techniques to evolve new perturbative and constructive heuristics. The low-level heuristics (representing graph transformations) are evolved using a single grammar which is capable of adapting to multiple domains. We test our generators of heuristics on instances of the Travelling Salesman Problem, Knapsack Problem and Load Balancing Problem and show that the best evolved heuristics can compete with human written heuristics and representations designed for each problem domain. Further we propose a conceptual framework for the production and combination of graph structures. We show how these concepts can be used to describe and provide a representation for problems in combinatorics and the inner mechanics of hyper-heuristic systems. The final contribution is a new benchmark that can generate problem instances for multiple problem domains that can be used for the assessment of multi-domain problem solvers