José-Luis Sancho-Gómez

K nearest neighbours with mutual information for simultaneous classification and missing data imputation

Missing data is a common drawback in many real-life pattern classification scenarios. One of the most popular solutions is missing data imputation by the K nearest neighbours (KNN) algorithm. In this article, we propose a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI). This method provides a missing data estimation aimed at solving the classification task, i.e., it provides an imputed dataset which is directed toward improving the classification performance. The MI-based distance metric is also used to implement an effective KNN classifier. Experimental results on both artificial and real classification datasets are provided to illustrate the efficiency and the robustness of the proposed algorithm.

Neural networks letter: Neural architecture design based on extreme learning machine

Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages.

Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks

Atherosclerosis is the leading underlying pathologic process that results in cardiovascular diseases, which represents the main cause of death and disability in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. The intima-media thickness (IMT) of the common carotid artery (CCA) has emerged as one of the most powerful tool for the evaluation of preclinical atherosclerosis. IMT is measured by means of B-mode ultrasound images, which is a non-invasive and relatively low-cost technique. This paper proposes an effective image segmentation method for the IMT measurement in an automatic way. With this purpose, segmentation is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task. In particular, multi-layer perceptrons trained under the scaled conjugate gradient algorithm have been used. The suggested approach is tested on a set of 60 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings. Moreover, the intra- and inter-observer errors have also been assessed. Despite of the simplicity of our approach, several quantitative statistical evaluations have shown its accuracy and robustness.

Classifying patterns with missing values using Multi-Task Learning perceptrons

Datasets with missing values are frequent in real-world classification problems. It seems obvious that imputation of missing values can be considered as a series of secondary tasks, while classification is the main purpose of any machine dealing with these datasets. Consequently, Multi-Task Learning (MTL) schemes offer an interesting alternative approach to solve missing data problems. In this paper, we propose an MTL-based method for training and operating a modified Multi-Layer Perceptron (MLP) architecture to work in incomplete data contexts. The proposed approach achieves a balance between both classification and imputation by exploiting the advantages of MTL. Extensive experimental comparisons with well-known imputation algorithms show that this approach provides excellent results. The method is never worse than the traditional algorithms - an important robustness property - and, also, it clearly outperforms them in several problems.

Fully automatic segmentation of ultrasound common carotid artery images based on machine learning

Abstract Atherosclerosis is responsible for a large proportion of cardiovascular diseases (CVD), which are the leading cause of death in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. It causes thickening and the reduction of elasticity in the blood vessels. An early diagnosis of this condition is crucial to prevent patients from suffering more serious pathologies (heart attacks and strokes). The evaluation of the Intima-Media Thickness (IMT) of the Common Carotid Artery (CCA) in B-mode ultrasound images is considered the most useful tool for the investigation of preclinical atherosclerosis. Usually, it is manually measured by the radiologists. This paper proposes a fully automatic segmentation technique based on Machine Learning and Statistical Pattern Recognition to measure IMT from ultrasound CCA images. The pixels are classified by means of artificial neural networks to identify the IMT boundaries. Moreover, the concepts of Auto-Encoders (AE) and Deep Learning have been included in the classification strategy. The suggested approach is tested on a set of 55 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings.

Saturated Perceptrons for Maximum Margin and Minimum Misclassification Error

This Letter discusses the application of gradient-based methods to train a single layer perceptron subject to the constraint that the saturation degree of the sigmoid activation function (measured as its maximum slope in the sample space) is fixed to a given value. From a theoretical standpoint, we show that, if the training set is not linearly separable, the minimization of an Lp error norm provides an approximation to the minimum error classifier, provided that the perceptron is highly saturated. Moreover, if data are linearly separable, the perceptron approximates the maximum margin classifier

Pattern Classification with Missing Values using Multitask Learning

In many real-life applications it is important to know how to deal with missing data (incomplete feature vectors). The ability of handling missing data has become a fundamental requirement for pattern classification because inappropriate treatment of missing data may cause large errors or false results on classification. A novel effective neural network is proposed to handle missing values in incomplete patterns with multitask learning (MTL). In our approach, a MTL neural network learns in parallel the classification task and the different tasks associated to incomplete features. During the MTL process, missing values are estimated or imputed. Missing data imputation is guided and oriented by the classification task, i.e., imputed values are those that contribute to improve the learning. We prove the robustness of this MTL neural network for handling missing values in classification problems from UCI database.

Exploiting multitask learning schemes using private subnetworks

Many problems in pattern recognition are focused to learn one main task, SingleTaskLearning (STL). However, most of them can be formulated from learning several tasks related to the main task at the same time while using a shared representation, MultitaskLearning (MTL). In this paper, a new MLT architecture is proposed and its performance is compared with those obtained from other previous schemes used in MTL. This new MTL scheme makes use of private subnetworks to induce a bias in the learning process. The results provided from artificial and real data sets show how the use of this private subnetworks in MTL produces a better generalization capabilities and a faster learning.

Multi-task Neural Networks for Dealing with Missing Inputs

Incomplete data is a common drawback in many pattern classification applications. A classical way to deal with unknown values is missing data estimation. Most machine learning techniques work well with missing values, but they do not focus the missing data estimation to solve the classification task. This paper presents effective neural network approaches based on Multi-Task Learning (MTL) for pattern classification with missing inputs. These MTL networks are compared with representative procedures used for handling incomplete data on two well-known data sets. The experimental results show the superiority of our approaches with respect to alternative techniques.

Related Tasks Selection to Multitask Learning Schemes

In Multitask Learning (MTL), a task is learned together with other related tasks, producing a transfer of information between them which can be advantageous for learning of the first one. However, rarely can solve a problem under an MTL scheme since no data are available that satisfying the conditions that need a MTL scheme. This paper presents a method to detect related tasks with the main one that allow to implement a multitask learning scheme. The method use the advantages of the Extreme Learning Machine and selects the secondary tasks without testing/error methodologies that increase the computational complexity.