Semi-supervised learning and fairness-aware learning under class imbalance

With the advent of Web 2.0 and the rapid technological advances, there is a plethora of data in every field; however, more data does not necessarily imply more information, rather the quality of data (veracity aspect) plays a key role. Data quality is a major issue, since machine learning algorithms are solely based on historical data to derive novel hypotheses. Data may contain noise, outliers, missing values and/or class labels, and skewed data distributions. The latter case, the so-called class-imbalance problem, is quite old and still affects dramatically machine learning algorithms. Class-imbalance causes classification models to learn effectively one particular class (majority) while ignoring other classes (minority). In extend to this issue, machine learning models that are applied in domains of high societal impact have become biased towards groups of people or individuals who are not well represented within the data. Direct and indirect discriminatory behavior is prohibited by international laws; thus, there is an urgency of mitigating discriminatory outcomes from machine learning algorithms. In this thesis, we address the aforementioned issues and propose methods that tackle class imbalance, and mitigate discriminatory outcomes in machine learning algorithms. As part of this thesis, we make the following contributions: • Tackling class-imbalance in semi-supervised learning – The class-imbalance problem is very often encountered in classification. There is a variety of methods that tackle this problem; however, there is a lack of methods that deal with class-imbalance in the semi-supervised learning. We address this problem by employing data augmentation in semi-supervised learning process in order to equalize class distributions. We show that semi-supervised learning coupled with data augmentation methods can overcome class-imbalance propagation and significantly outperform the standard semi-supervised annotation process. • Mitigating unfairness in supervised models – Fairness in supervised learning has received a lot of attention over the last years. A growing body of pre-, in- and postprocessing approaches has been proposed to mitigate algorithmic bias; however, these methods consider error rate as the performance measure of the machine learning algorithm, which causes high error rates on the under-represented class. To deal with this problem, we propose approaches that operate in pre-, in- and post-processing layers while accounting for all classes. Our proposed methods outperform state-of-the-art methods in terms of performance while being able to mitigate unfair outcomes

Delineating Knowledge Domains in Scientific Domains in Scientific Literature using Machine Learning (ML)

The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like data preparation followed by processing, transformation, and application of classification techniques culminating in the validation of the results

Facial Action Recognition Combining Heterogeneous Features via Multi-Kernel Learning

International audienceThis paper presents our response to the first interna- tional challenge on Facial Emotion Recognition and Analysis. We propose to combine different types of features to automatically detect Action Units in facial images. We use one multi-kernel SVM for each Action Unit we want to detect. The first kernel matrix is computed using Local Gabor Binary Pattern histograms and a histogram intersection kernel. The second kernel matrix is computed from AAM coefficients and an RBF kernel. During the training step, we combine these two types of features using the recently proposed SimpleMKL algorithm. SVM outputs are then averaged to exploit temporal information in the sequence. To eval- uate our system, we perform deep experimentations on several key issues: influence of features and kernel function in histogram- based SVM approaches, influence of spatially-independent in- formation versus geometric local appearance information and benefits of combining both, sensitivity to training data and interest of temporal context adaptation. We also compare our results to those of the other participants and try to explain why our method had the best performance during the FERA challenge

Text Categorization and Machine Learning Methods: Current State Of The Art

In this informative age, we find many documents are available in digital forms which need classification of the text. For solving this major problem present researchers focused on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of pre classified documents, the characteristics of the categories. The main benefit of the present approach is consisting in the manual definition of a classifier by domain experts where effectiveness, less use of expert work and straightforward portability to different domains are possible. The paper examines the main approaches to text categorization comparing the machine learning paradigm and present state of the art. Various issues pertaining to three different text similarity problems, namely, semantic, conceptual and contextual are also discussed

Enhanced face detection framework based on skin color and false alarm rejection

Fast and precise face detection is a challenging task in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as recognition tracking, and image database management. In the applications, face objects often come from an inconsequential part of images that contain variations namely different illumination, pose, and occlusion. These variations can decrease face detection rate noticeably. Besides that, detection time is an important factor, especially in real time systems. Most existing face detection approaches are not accurate as they have not been able to resolve unstructured images due to large appearance variations and can only detect human face under one particular variation. Existing frameworks of face detection need enhancement to detect human face under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework was proposed to improve detection rate based on skin color and provide a validity process. A preliminary segmentation of input images based on skin color can significantly reduce search space and accelerate the procedure of human face detection. The main detection process is based on Haar-like features and Adaboost algorithm. A validity process is introduced to reject non-face objects, which may be selected during a face detection process. The validity process is based on a two-stage Extended Local Binary Patterns. Experimental results on CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. As a conclusion, the proposed enhanced face detection framework in color images with the presence of varying lighting conditions and under different poses has resulted in high detection rate and reducing overall detection time