Estudio de métodos de construcción de ensembles de clasificadores y aplicaciones

La inteligencia artificial se dedica a la creación de sistemas informáticos con un comportamiento inteligente. Dentro de este área el aprendizaje computacional estudia la creación de sistemas que aprenden por sí mismos. Un tipo de aprendizaje computacional es el aprendizaje supervisado, en el cual, se le proporcionan al sistema tanto las entradas como la salida esperada y el sistema aprende a partir de estos datos. Un sistema de este tipo se denomina clasificador. En ocasiones ocurre, que en el conjunto de ejemplos que utiliza el sistema para aprender, el número de ejemplos de un tipo es mucho mayor que el número de ejemplos de otro tipo. Cuando esto ocurre se habla de conjuntos desequilibrados. La combinación de varios clasificadores es lo que se denomina "ensemble", y a menudo ofrece mejores resultados que cualquiera de los miembros que lo forman. Una de las claves para el buen funcionamiento de los ensembles es la diversidad. Esta tesis, se centra en el desarrollo de nuevos algoritmos de construcción de ensembles, centrados en técnicas de incremento de la diversidad y en los problemas desequilibrados. Adicionalmente, se aplican estas técnicas a la solución de varias problemas industriales.Ministerio de Economía y Competitividad, proyecto TIN-2011-2404

Meta-raps: Parameter Setting And New Applications

Recently meta-heuristics have become a popular solution methodology, in terms of both research and application, for solving combinatorial optimization problems. Meta-heuristic methods guide simple heuristics or priority rules designed to solve a particular problem. Meta-heuristics enhance these simple heuristics by using a higher level strategy. The advantage of using meta-heuristics over conventional optimization methods is meta-heuristics are able to find good (near optimal) solutions within a reasonable computation time. Investigating this line of research is justified because in most practical cases with medium to large scale problems, the use of meta-heuristics is necessary to be able to find a solution in a reasonable time. The specific meta-heuristic studied in this research is, Meta-RaPS; Meta-heuristic for Randomized Priority Search which is developed by DePuy and Whitehouse in 2001. Meta-RaPS is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element (Moraga, 2002). To date, Meta-RaPS had been applied to different types of combinatorial optimization problems and achieved comparable solution performance to other meta-heuristic techniques. The specific problem studied in this dissertation is parameter setting of Meta-RaPS. The topic of parameter setting for meta-heuristics has not been extensively studied in the literature. Although the parameter setting method devised in this dissertation is used primarily on Meta-RaPS, it is applicable to any meta-heuristic\u27s parameter setting problem. This dissertation not only enhances the power of Meta-RaPS by parameter tuning but also it introduces a robust parameter selection technique with wide-spread utility for many meta-heuristics. Because the distribution of solution values generated by meta-heuristics for combinatorial optimization problems is not normal, the current parameter setting techniques which employ a parametric approach based on the assumption of normality may not be appropriate. The proposed method is Non-parametric Based Genetic Algorithms. Based on statistical tests, the Non-parametric Based Genetic Algorithms (NPGA) is able to enhance the solution quality of Meta-RaPS more than any other parameter setting procedures benchmarked in this research. NPGA sets the best parameter settings, of all the methods studied, for 38 of the 41 Early/Tardy Single Machine Scheduling with Common Due Date and Sequence-Dependent Setup Time (ETP) problems and 50 of the 54 0-1 Multidimensional Knapsack Problems (0-1 MKP). In addition to the parameter setting procedure discussed, this dissertation provides two Meta-RaPS combinatorial optimization problem applications, the 0-1 MKP, and the ETP. For the ETP problem, the Meta-RaPS application in this dissertation currently gives the best meta-heuristic solution performance so far in the literature for common ETP test sets. For the large ETP test set, Meta-RaPS provided better solution performance than Simulated Annealing (SA) for 55 of the 60 problems. For the small test set, in all four different small problem sets, the Meta-RaPS solution performance outperformed exiting algorithms in terms of average percent deviation from the optimal solution value. For the 0-1 MKP, the present Meta-RaPS application performs better than the earlier Meta-RaPS applications by other researchers on this problem. The Meta-RaPS 0-1 MKP application presented here has better solution quality than the existing Meta-RaPS application (Moraga, 2005) found in the literature. Meta-RaPS gives 0.75% average percent deviation, from the best known solutions, for the 270 0-1 MKP test problems

Biased landscapes for random Constraint Satisfaction Problems

The typical complexity of Constraint Satisfaction Problems (CSPs) can be investigated by means of random ensembles of instances. The latter exhibit many threshold phenomena besides their satisfiability phase transition, in particular a clustering or dynamic phase transition (related to the tree reconstruction problem) at which their typical solutions shatter into disconnected components. In this paper we study the evolution of this phenomenon under a bias that breaks the uniformity among solutions of one CSP instance, concentrating on the bicoloring of k-uniform random hypergraphs. We show that for small k the clustering transition can be delayed in this way to higher density of constraints, and that this strategy has a positive impact on the performances of Simulated Annealing algorithms. We characterize the modest gain that can be expected in the large k limit from the simple implementation of the biasing idea studied here. This paper contains also a contribution of a more methodological nature, made of a review and extension of the methods to determine numerically the discontinuous dynamic transition threshold.Comment: 32 pages, 16 figure

Learning Probabilistic Generative Models For Fast Sampling-Based Planning

Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes of confidence ellipsoids of GMMs learned from prior collision history. For efficient sampling, we suggest a sampling method with a learned Q-function with linear function approximation based on feature representations such as Radial Basis Functions. This sampling method chooses the optimal node from which to extend the search tree via the softmax function of learned state values. We also discuss a novel constrained sampling-based motion planning method for grasp and transport tasks with redundant robotic manipulators, which allows the best grasp configuration and approach direction to be automatically determined. Since these approaches with the learned probabilistic models require large size data and time for training, it is essential that they are able to be adapted to environmental change in an online manner. The suggested online learning approach with the Dirichlet Process Mixture Model (DPMM) can adapt the complexity to the data and learn new Gaussian clusters with streaming data in newly explored areas without batch learning. We have applied these approaches in a number of robot arm planning scenarios and have shown their utility and effectiveness in simulation and on a physical 7-DoF robot manipulator

Prescriptive formalism for constructing domain-specific evolutionary algorithms

It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m..

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Effects of non-linearities and disorder in systems with multiple absorbing states. A perspective for modeling the dynamics of complex ecosystems.

Interacting particle systems are a particular class of stochastic processes where single degrees of freedom interact through probabilistic rules defined over a graph which reflects the spatial topology of the model. From the statistical mechanics point of view these models are of particular interest since they are genuinely out-of-equilibrium processes and introduce new universality classes and dynamical phase transitions. Among these processes, systems with absorbing states are characterized by points in the state-space in which the dynamics becomes trivial and, once reached, cannot be leaved. Because of the several possible interpretations, these models have found many applications in different areas of science: from condensed matter Physics to Biology, from Ecology to Sociology and Finance and also, in their quantum versions, to quantum control theory. Despite their importance for possible applications, though, a unified understanding of these systems is still lacking. In theoretical ecology, many open fundamental questions about the dynamics of ecosystems provide the cue for a further development of the theory of interacting particle systems. In particular, in this thesis we will address three main topics: i) Spontaneous neutral symmetry breaking. A central problem in ecology is the elucidation of the mechanisms responsible for biodiversity and stability. Neutral theory provides gross patterns in accord with empirical observations, but its validity is still highly debated. In particular, it is not clear how this theory can originate the observed non-neutral dynamics. Within a completely species-symmetric theory, we demonstrate that nonlinear dynamics can lead to a stationary state characterized by both stability and biodiversity by spontaneously breaking the neutral symmetry. ii) Habitat heterogeneities. It is known that habitat can have a great impact on the dynamics of species. In its most basic level of abstraction, its effects can be mimicked by an interacting particle system in a quenched random external field that locally breaks the species symmetry. We propose here an effective solution of the model in the long-times limit. iii) Role of boundary conditions. For non-equilibrium systems near a critical point, little is known about the role of the boundary conditions to the global phase diagram of the system. We analyze here a paradigmatic non-equilibrium critical model with mixed symmetry-preserving boundary conditions