Using DEA to estimate the importance of objectives for decision makers

In this paper we establish further connections between DEA and Multi-criteria Decision Analysis by suggesting a particular way to estimate preference weights for different objectives using DEA. We claim that the virtual multipliers obtained from a standard DEA model are not suitable to measure the preferences of a decision maker. Our suggestion takes advantage of the parallelism between DEA and the methodology proposed by Sumpsi et al. (1997) by projecting each unit on a linear combination of the elements of the pay-off matrix. Finally, we make an application of the proposed methodology to agricultural economics in a case study with Spanish data.Data Envelopment Analysis, Multicriteria Decision Analysis, preferences, weights, virtual multipliers.

Convex Optimization for Binary Classifier Aggregation in Multiclass Problems

Multiclass problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. Various methods, including all-pairs (APs), one-versus-all (OVA), and error correcting output code (ECOC), have been studied, to decompose multiclass problems into binary problems. However, little study has been made to optimally aggregate binary problems to determine a final answer to the multiclass problem. In this paper we present a convex optimization method for an optimal aggregation of binary classifiers to estimate class membership probabilities in multiclass problems. We model the class membership probability as a softmax function which takes a conic combination of discrepancies induced by individual binary classifiers, as an input. With this model, we formulate the regularized maximum likelihood estimation as a convex optimization problem, which is solved by the primal-dual interior point method. Connections of our method to large margin classifiers are presented, showing that the large margin formulation can be considered as a limiting case of our convex formulation. Numerical experiments on synthetic and real-world data sets demonstrate that our method outperforms existing aggregation methods as well as direct methods, in terms of the classification accuracy and the quality of class membership probability estimates.Comment: Appeared in Proceedings of the 2014 SIAM International Conference on Data Mining (SDM 2014

Analysis of altitude sickness by Lake Louise Score prediction and ophthalmological data studies

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Oriol Pujol Vila[en] In this work we analyse the data obtained from the Sherpa 2017 Everest project. We focus the study on two problems. First, we study the cause of altitude sickness by analysing the factors that influence the most in the prediction of the Lake Louse Score. Second we study the affection of damage by hypoxia in ophthalmic data. In order to help with this studies, we propose the Iterative Backward Relaxed SVM selection method. This method sorts the factors that are related to the prediction result. With the obtained ordered factors list, we perform the feature selection to remove the uncorrelated factors. The prediction of both Lake Louise Score prediction and the ophthalmic data studies got positive results

Long-Run Network Pricing for Security of Supply in Distribution Networks

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Improving Efficiency in Deep Learning for Large Scale Visual Recognition

The emerging recent large scale visual recognition methods, and in particular the deep Convolutional Neural Networks (CNN), are promising to revolutionize many computer vision based artificial intelligent applications, such as autonomous driving and online image retrieval systems. One of the main challenges in large scale visual recognition is the complexity of the corresponding algorithms. This is further exacerbated by the fact that in most real-world scenarios they need to run in real time and on platforms that have limited computational resources. This dissertation focuses on improving the efficiency of such large scale visual recognition algorithms from several perspectives. First, to reduce the complexity of large scale classification to sub-linear with the number of classes, a probabilistic label tree framework is proposed. A test sample is classified by traversing the label tree from the root node. Each node in the tree is associated with a probabilistic estimation of all the labels. The tree is learned recursively with iterative maximum likelihood optimization. Comparing to the hard label partition proposed previously, the probabilistic framework performs classification more accurately with similar efficiency. Second, we explore the redundancy of parameters in Convolutional Neural Networks (CNN) and employ sparse decomposition to significantly reduce both the amount of parameters and computational complexity. Both inter-channel and inner-channel redundancy is exploit to achieve more than 90\% sparsity with approximately 1\% drop of classification accuracy. We also propose a CPU based efficient sparse matrix multiplication algorithm to reduce the actual running time of CNN models with sparse convolutional kernels. Third, we propose a multi-stage framework based on CNN to achieve better efficiency than a single traditional CNN model. With a combination of cascade model and the label tree framework, the proposed method divides the input images in both the image space and the label space, and processes each image with CNN models that are most suitable and efficient. The average complexity of the framework is significantly reduced, while the overall accuracy remains the same as in the single complex model

Jack of all trades, Master of None::The trade-offs in sparse PCA methods for diverse purposes

Sparse algorithms are becoming increasingly popular in data science research because they can identify and select the most relevant variables in a dataset while minimizing overfitting. However, sparse algorithms present unique challenges when dealing with social data, such as data integration (heterogeneity) and the need to account for complex social interactions and dynamics. Throughout this thesis, I focused on researching the sparse Principal Component Analysis (sPCA) problem. I have explored and developed sPCA algorithms that can effectively identify and select the essential features in a dataset, reducing its dimensionality or underlying factors in the data. Specifically, I examined sPCA methods that utilize sparsity-inducing penalties and cardinality constraints to achieve sparsity in the solution