商品描述
This book gathers selected and peer-reviewed contributions presented at the 18th Conference of the International Federation of Classification Societies (IFCS 2024), held in San José, Costa Rica, July 15-19, 2024. Covering a wide range of topics, it describes modern methods and real-world applications in data science, classification, and artificial intelligence related to modeling decision making. Numerous novel techniques and innovative applications are investigated, such as anomaly detection in public procurement processes, multivariate functional data clustering, air pollution prediction, benchmark generation for probabilistic planning, recommendation systems based on symbolic data analysis, and methods for clustering mixed-type data. Advanced statistical concepts are explored, including Vapnik-Chervonenkis dimensionality, Riemannian statistics, hypothesis testing for interval-valued data, and mixed models. Furthermore, machine learning techniques are applied to predict soil bacterial and fungal communities, classify electoral behavior and political competition, and assess corrosion degradation in mining pipelines. The diversity of topics discussed in this collection reflects the ongoing advancement and interdisciplinary nature of statistical and data science research, as well as its application across various fields and sectors. These studies contribute to the development of robust methodologies and efficient computational tools to address complex challenges in the era of big data. The book is intended for researchers and practitioners seeking the latest developments and applications in the field of data science and classification.
商品描述(中文翻譯)
本書匯集了在2024年國際分類學會聯合會（IFCS 2024）第18屆會議上所呈現的經過選擇和同行評審的貢獻，會議於2024年7月15日至19日在哥斯大黎加的聖荷西舉行。涵蓋廣泛的主題，本書描述了與決策建模相關的數據科學、分類和人工智慧的現代方法及其實際應用。
本書探討了許多新穎的技術和創新的應用，例如公共採購過程中的異常檢測、多變量函數數據聚類、空氣污染預測、概率規劃的基準生成、基於符號數據分析的推薦系統，以及混合型數據的聚類方法。進一步探討了高級統計概念，包括Vapnik-Chervonenkis維度、黎曼統計、區間值數據的假設檢驗和混合模型。此外，還應用機器學習技術來預測土壤細菌和真菌群落、分類選舉行為和政治競爭，以及評估礦業管道中的腐蝕劣化。
本書中討論的主題多樣性反映了統計和數據科學研究的持續進展和跨學科特性，以及其在各個領域和行業中的應用。這些研究有助於開發穩健的方法論和高效的計算工具，以應對大數據時代的複雜挑戰。
本書旨在為尋求數據科學和分類領域最新發展和應用的研究人員和實務工作者提供參考。
作者簡介
Javier Trejos is a Full Professor and Researcher at the School of Mathematics and the Center for Research in Pure and Applied Mathematics (CIMPA), University of Costa Rica. His research focuses on the relations between data analysis and combinatorial optimization. He was the chief editor of the Journal of Mathematics: Theory and Applications and is the former president of the Central American and Caribbean Society for Classification and Data Analysis (SoCCCAD). In 1996 he was awarded the Simon Régnier Prize of the Francophone Classification Society. Theodore Chadjipandelis is Professor of Applied Statistics and the Director of the Laboratory of Applied Political Research, Aristotle University, Thessaloniki, Greece. His research interests are in the field of applied statistics and mainly focus on issues of experiment design, statistical research training, public opinion, political and electoral behavior, electoral geography, election systems as well as urban and regional programming and development. He coordinated the Greek section of the program C.C.S. (Comparative Candidates Survey) - a co-operation between 30 research teams - and of C.S.E.S. (Comparative Study of Electoral Systems). Currently he coordinates the Greek section of the program MeDem (Measuring Electoral Democracy) - a co-operation between 30 research teams - and of the Horizon project AI4GOV (Artificial Intelligence for Governance). Aurea Grané is Full Professor of Statistics and Operations Research at Universidad Carlos III de Madrid, Spain. Her work involves several lines of research whose common link is the development of non-parametric techniques based on distances with application to data of a certain complexity. She has important contributions in the development of goodness-of-fit statistics for uniformity, exponentiality and normality tests, in statistical methods based on distances for data visualization, in predictive methods for functional data and in the development of tools for outlier detection in long financial series and mixed data sets. Mario Villalobos is a Professor and Researcher at the University of Costa Rica, School of Mathematics, and the Center for Research in Pure and Applied Mathematics (CIMPA), of which he was its director until 2020, and a lecturer at the Costa Rica Institute of Technology. His research deals mainly with multi-objective optimization and its relationships with statistical and data analysis methods, the study of functions, teaching innovations in mathematics, and currently curve-fitting to see trends in epidemics. He was the recipient of the Chikio Hayashi Award from the International Federation of Classification Societies in 2006.
作者簡介(中文翻譯)
哈維爾·特雷霍斯是哥斯大黎加大學數學學院及純粹與應用數學研究中心（CIMPA）的全職教授及研究員。他的研究專注於數據分析與組合優化之間的關係。他曾擔任《數學：理論與應用期刊》的主編，並且是中美洲及加勒比海分類與數據分析學會（SoCCCAD）的前任會長。1996年，他獲得法語分類學會的西蒙·雷尼耶獎。
西奧多·查吉潘德利斯是希臘塞薩洛尼基亞里士多德大學的應用統計學教授及應用政治研究實驗室主任。他的研究興趣集中在應用統計學領域，主要涉及實驗設計、統計研究訓練、公共意見、政治與選舉行為、選舉地理、選舉系統以及城市和區域規劃與發展等問題。他協調了C.C.S.（比較候選人調查）計劃的希臘部分，該計劃是30個研究團隊之間的合作，並且協調了C.S.E.S.（比較選舉系統研究）。目前，他協調了MeDem（測量選舉民主）計劃的希臘部分，這也是30個研究團隊之間的合作，並且參與了Horizon項目AI4GOV（治理的人工智慧）。
奧雷亞·格拉內是西班牙馬德里卡洛斯三世大學的統計學與運籌學全職教授。她的工作涉及多條研究方向，其共同點是基於距離的非參數技術的發展，並應用於某些複雜數據。她在均勻性、指數性和正態性檢驗的擬合優度統計量的發展、基於距離的數據可視化統計方法、功能數據的預測方法以及在長期金融序列和混合數據集中的異常值檢測工具的發展方面有重要貢獻。
馬里奧·比利亞洛博斯是哥斯大黎加大學數學學院及純粹與應用數學研究中心（CIMPA）的教授及研究員，並曾擔任該中心的主任直到2020年，同時也是哥斯大黎加科技學院的講師。他的研究主要涉及多目標優化及其與統計和數據分析方法的關係、函數研究、數學教學創新，以及目前的曲線擬合以觀察疫情趨勢。他於2006年獲得國際分類學會聯合會的林千雄獎。
