Dimensionality Reduction for Classification: Comparison of Techniques and Dimension Choice
CitationThis paper is published in Lecture Notes in Artificial Intelligence 5139, p411-418, Springer
Data-Mining
Passenger Name Record Data Mining Based Cancellation Forecasting for Revenue Management
Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation … Read more
Concave programming for minimizing the zero-norm over polyhedral sets
Given a non empty polyhedral set, we consider the problem of finding a vector belonging to it and having the minimum number of nonzero components, i.e., a feasible vector with minimum zero-norm. This nonsmooth combinatorial optimization problem is NP-Hard and arises in various fields such as machine learning, pattern recognition, signal processing. We propose two … Read more
Alternating Local Search Based VNS for Linear Classification
CitationThis paper is published in Annals of Operations Research, Springer
Kernel Support Vector Regression with imprecise output
We consider a regression problem where uncertainty affects to the dependent variable of the elements of the database. A model based on the standard epsilon-Support Vector Regression approach is given, where two hyperplanes need to be constructed to predict the interval-valued dependent variable. By using the Hausdorff distance to measure the error between predicted and … Read more
Building separating concentric balls to solve a multi-instance classification problem
In this work, we consider a classification problem where the objects to be classified are bags of instances which are vectors measuring d different attributes. The classification rule is defined in terms of a ball, whose center and radius are the parameters to be computed. Given a bag, it is assigned to the positive class … Read more
Support Vector Regression for imprecise data
In this work, a regression problem is studied where the elements of the database are sets with certain geometrical properties. In particular, our model can be applied to handle data affected by some kind of noise or uncertainty and interval-valued data, and databases with missing values as well. The proposed formulation is based on the … Read more
Adaptive Constraint Reduction for Training Support Vector Machines
A support vector machine (SVM) determines whether a given observed pattern lies in a particular class. The decision is based on prior training of the SVM on a set of patterns with known classification, and training is achieved by solving a convex quadratic programming problem. Since there are typically a large number of training patterns, … Read more
Enclosing Machine Learning
This report introduces a new machine learning paradigm called enclosing machine learning for data mining. This novel method utilizes the virtues of human being’s cognition process and tries to imitate the two basic principles of cognition process from a macroscopical view, which are cognizing things of the same kind, recognizing things of a new kind … Read more
Classification problems with imprecise data through separating hyperplanes
We consider a supervised classification problem in which the elements to be classified are sets with certain geometrical properties. In particular, this model can be applied to deal with data affected by some kind of noise and in the case of interval-valued data. Two classification rules, a fuzzy one and a crisp one, are defined … Read more