{"product_id":"cost-sensitive-machine-learning","title":"Cost-Sensitive Machine Learning","description":"\u003cp\u003eIn machine learning applications, practitioners must take into account the \u003cem\u003ecost \u003c\/em\u003eassociated with the algorithm. These costs include: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eCost of acquiring training data\u003c\/li\u003e\n\u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eCost of data annotation\/labeling and cleaning\u003c\/li\u003e\n\u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eComputational cost for model fitting, validation, and testing\u003c\/li\u003e\n\u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eCost of collecting features\/attributes for test data\u003c\/li\u003e\n\u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eCost of user feedback collection\u003c\/li\u003e\n\u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eCost of incorrect prediction\/classification\u003c\/li\u003e\n\u003cli\u003e\n\u003cbr\u003e\u003cbr\u003e \u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCost-Sensitive Machine Learning\u003c\/strong\u003e is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eSpurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":54243035677016,"sku":"9780367381912","price":78.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9780367381912.jpg?v=1777696241","url":"https:\/\/agendabookshop.com\/products\/cost-sensitive-machine-learning","provider":"Agenda Bookshop","version":"1.0","type":"link"}