10 research outputs found
Multiclass optimal classification trees with SVM‑splits
In this paper we present a novel mathematical optimization-based methodology to construct
tree-shaped classification rules for multiclass instances. Our approach consists of
building Classification Trees in which, except for the leaf nodes, the labels are temporarily
left out and grouped into two classes by means of a SVM separating hyperplane. We provide
a Mixed Integer Non Linear Programming formulation for the problem and report the
results of an extended battery of computational experiments to assess the performance of
our proposal with respect to other benchmarking classification methods.Universidad de Sevilla/CBUASpanish Ministerio de Ciencia y Tecnología, Agencia Estatal de Investigación, and
Fondos Europeos de Desarrollo Regional (FEDER) via project PID2020-114594GB-C21Junta de Andalucía
projects FEDER-US-1256951, P18-FR-1422, CEI-3-FQM331, B-FQM-322-UGR20AT 21_00032;
Fundación BBVA through project NetmeetData: Big Data 2019UE-NextGenerationEU (ayudas de movilidad
para la recualificación del profesorado universitario)IMAG-Maria de Maeztu grant CEX2020-
001105-M /AEI /10.13039/50110001103
Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution
https://ieeexplore.ieee.org/document/8632897Breast cancer prognostic modeling is difficult since it is governed by many diverse factors. Given the low median survival and large scale breast cancer data, which comes from high throughput technology, the accurate and reliable prognosis of breast cancer is becoming increasingly difficult. While accurate and timely prognosis may save many patients from going through painful and expensive treatments, it may also help oncologists in managing the disease more efficiently and effectively. Data analytics augmented by machine-learning algorithms have been proposed in past for breast cancer prognosis; and however, most of these could not perform well owing to the heterogeneous nature of available data and model interpretability related issues. A robust prognostic modeling approach is proposed here whereby a Pareto optimal set of deep neural networks (DNNs) exhibiting equally good performance metrics is obtained. The set of DNNs is initialized and their hyperparameters are optimized using the evolutionary algorithm, NSGAIII. The final DNN model is selected from the Pareto optimal set of many DNNs using a fuzzy inferencing approach. Contrary to using DNNs as the black box, the proposed scheme allows understanding how various performance metrics (such as accuracy, sensitivity, F1, and so on) change with changes in hyperparameters. This enhanced interpretability can be further used to improve or modify the behavior of DNNs. The heterogeneous breast cancer database requires preprocessing for better interpretation of categorical variables in order to improve prognosis from classifiers. Furthermore, we propose to use a neural network-based entity-embedding method for categorical features with high cardinality. This approach can provide a vector representation of categorical features in multidimensional space with enhanced interpretability. It is shown with evidence that DNNs optimized using evolutionary algorithms exhibit improved performance over other classifiers mentioned in this paper
Реализация методов классификации людей по полу и возрасту и их повторной идентификации в видеопотоке с помощью технологий глубокого обучения
В настоящее время всё большей популярностью пользуются интеллектуальные системы видеонаблюдения, способные автоматизировать аналитику отслеживаемых объектов. В работе предложены алгоритмы классификации людей по полу и возрасту, а также создания анонимных индивидуальных отпечатков объектов по графическим признакам для обеспечения повторного распознавания. Алгоритмы основаны на использовании методов глубокого обучения на наборах ограничивающих окон треклетов объектов.Currently, intelligent video surveillance systems capable of automating the analytics of tracked objects are becoming increasingly popular. The paper proposes algorithms for classifying people by gender and age, as well as creating anonymous individual prints of objects based on graphic features to ensure repeated recognition. The algorithms are based on the use of deep learning methods on sets of bounding windows of object tracklets
Improving multiclass classification by deep networks using DAGSVM and Triplet Loss
With recent advances in the field of computer vision and especially deep learning, many fully connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification and natural language processing. For classification tasks however, most of these deep learning models employ the softmax activation function for prediction and minimize cross-entropy loss. In contrast, we demonstrate a consistent advantage by replacing the softmax layer by a set of binary SVM classifiers organized in a tree or DAG (Directed Acyclic Graph) structure. The idea is to not treat the multiclass classification problem as a whole but to break it down into smaller binary problems where each classifier acts as an expert by focusing on differentiating between only two classes and thus improves the overall accuracy. Furthermore, by arranging the classifiers in a DAG structure, we later also show how it is possible to further improve the performance of the binary classifiers by learning more discriminative features through the same deep network. We validated the proposed methodology on two benchmark datasets, and the results corroborated our claim
Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science
These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)