Machine Learning and Knowledge Extraction, Volume 1, Issue 3

The modeling of time to event data is an important topic with many applications in diverse areas. The collective of methods to analyze such data are called survival analysis, event history analysis or duration analysis. Survival analysis is widely ap...

Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of th...

Prediction is a common machine learning (ML) technique used on building energy consumption data. This process is valuable for anomaly detection, load profile-based building control and measurement and verification procedures. Hundreds of building ene...

In real world applications, binary classification is often affected by imbalanced classes. In this paper, a new methodology to solve the class imbalance problem that occurs in image classification is proposed. A digital image is described through a n...

Statistical hypothesis testing is among the most misunderstood quantitative analysis methods from data science. Despite its seeming simplicity, it has complex interdependencies between its procedural components. In this paper, we discuss the underlyi...

In recent years the emergence of social media has become more prominent than ever. Social networking has become the de facto tool used by people all around the world for information discovery. Consequently, the importance of recommendations in a soci...

Recognizing objects and estimating their poses have a wide range of application in robotics. For instance, to grasp objects, robots need the position and orientation of objects in 3D. The task becomes challenging in a cluttered environment with diffe...

The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under sp...

In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The...

The need to detect outliers or otherwise unusual data, which can be formalized as the estimation a particular quantile of a distribution, is an important problem that frequently arises in a variety of applications of pattern recognition, computer vis...

Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press article...

Point estimation of class prevalences in the presence of dataset shift has been a popular research topic for more than two decades. Less attention has been paid to the construction of confidence and prediction intervals for estimates of class prevale...

Graphs are a very useful framework for representing information. In general, these data structures are used in different application domains where data of interest are described in terms of local and spatial relations. In this context, the aim is to...

The analysis of knee kinematic data, which come in the form of a small sample of discrete curves that describe repeated measurements of the temporal variation of each of the knee three fundamental angles of rotation during a subject walking cycle, ca...

This paper presents a semi-supervised faster region-based convolutional neural network (SF-RCNN) approach to detect persons and to classify the load carried by them in video data captured from distances several miles away via high-power lens video ca...