A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES

A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique

Classification of EEG signals of user states in gaming using machine learning

In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and k-nearest neighbor classify the three user states -- Boredom, Flow, and Anxiety? (ii) Can we distinguish the flow state from other user states using machine learning models? (iii) What are the essential components of EEG signals for classifying the three user states? To extract the critical components of EEG signals, a feature selection method known as minimum redundancy and maximum relevance method was implemented. An average accuracy of 85 % is achieved for classifying the three user states by using the support vector machine classifier --Abstract, page iii

Evaluation of a prior-incorporated statistical model and established classifiers for externally visible characteristics prediction

Human identification through DNA has played an important role in forensic science and in the criminal justice system for decades. It is referring to the association of genetic data with a particular human being and has facilitated police investigations in cases such as the identification of suspected perpetrators from biological traces found at crime scenes, missing persons, or victims of mass disasters [1]. Currently there are two main methods developed: the genotyping through short tandem repeats (STR profiling) and the forensic DNA phenotyping (FDP). Despite the fact that these two methods are aiming in identifying a person through its genetic material, their approach and consequences that come up are completely different. STR profiling compares allele repeats at specific loci in DNA and aims at a match with already known to the police authorities DNA profiles, while FDP, which is the focus on the current study, aims in the prediction of appearance traits of an individual [2, 3]. In contrast with STR profiling, information that arise out of FDP cannot be used as sole evidence in the court [4]. The ability of predicting EVCs from DNA can be used as ‘biological witnesses’ that can only provide leads for the investigative authorities and subsequently narrow down a possible large set of potential suspects. The use of FDP begins a new era of ‘DNA intelligence’ and holds great promise especially in cases where individuals cannot be identified with the conventional method of STR profiling and also in cases where there is no additional knowledge on the sample donor. So far in FDP, traits such as eye, hair and skin color can be predicted reliably with high prediction accuracy and predictive models have already been forensically validated [5-7]. Regarding other appearance traits, the current lack of knowledge on the genetic markers responsible for their phenotypic variation and the lower predictability, especially of intermediate categories, has prevented FDP from being routinely implemented in the field of forensic science. The majority of the predictive models developed for appearance trait prediction were based on multinomial logistic regression (MLR) while only few used other methods such as decision trees and neural networks. Machine learning (ML) approaches have become a widely used tool for classification problems in several fields and they are known for their potential to boost model performance and their ability to handle different and complex types of data [8]. However, within the context of predicting EVCs, a systematic and comparative analysis among different ML approaches that could possibly indicate methods that outperform the standard MLR, has not been conducted so far. In addition, incorporation of priors in the EVC prediction models that may have potential to improve the already existing approaches, has not been investigated in the context of forensics yet. These priors indicate the trait category prevalence values among biogeographic ancestry groups, and their use would allow us to leverage Bayesian statistics in order to build more powerful prediction models. In our case, incorporation of such priors in the model could reflect the additional information from all yet unknown causal genetic factors and act as proxies in the prediction model. Therefore, those two approaches were conducted throughout my PhD project in order to improve the already existing approaches of FDP which was the main aim of my study. In the first study, I aimed to collect a comprehensive data set from previously published sources on the spatial distribution of different appearance traits. I conducted a literature review in order to assemble this information, which later on could be incorporated as priors in the EVCs prediction models. Due to the lack of available and reliable sources, our resulting data set contained only eye and hair color for mostly European countries. More specifically, I collected data on eye color from 16 European and Central Asian countries, while for hair color I collected data from seven European countries. For countries outside of Europe, where the variation is low, it was not possible to assemble trustworthy and population-representative data. Afterwards, I calculated the association of those two traits and obtained a moderate association between them. Interpolation techniques were applied in order to infer trait prevalence values in at least neighboring countries. Resulting prevalences and interpolated values were presented in spatial maps. The subject of the second study was to incorporate the trait prevalence values as priors in the prediction model. However, due to the lack of reliable data that was observed in the first study, the incorporation of the actual priors that would give us the actual insight of their impact in the EVC prediction was not feasible with the current existing knowledge and the available data. Therefore, I assessed the impact of priors across a grid that contained all possible values that priors can take, for a set of appearance traits including eye, hair, skin color, hair structure, and freckles. In this way, I aimed to assess potential pitfalls caused by misspecification of priors. Results were compared and evaluated with the corresponding prior-free' previously established prediction models. The effect of priors was demonstrated in the standard performance measurements, including area under curve (AUC) and overall accuracy. I found out that from all possible prior values, there is a proportion that shows potential in improving the prediction accuracy. However, possible misspecification of priors can significantly diminish the overall accuracy. Based on that, I emphasize the importance of accurate prior values in the prediction modelling in order to identify the actual impact. As a consequence of the above, the use of prior informed models in forensics is currently infeasible and more studies on the topic are necessary in order to extend the current knowledge on spatial trait prevalence. Finally, the focus of the third study was exploring and comparing the performances of methodologies beyond MLR. MLR is considered the standard method for predicting EVCs, since the majority of the predictive models developed are based on that method. Due to the fact that there is still potential for improvement of MLR models, especially for traits such as skin color or hair structure, I aimed at applying different ML methods in order to identify whether there is a potential classifier that outperforms the conventional method of MLR. Therefore I conducted a systematic comparison between MLR and three alternative ML classifiers, namely support vector machines (SVM), random forests (RF) and artificial neural networks (ANN). The traits that I focused on here were eye, hair, and skin color. All models were based on the genetic markers that were previously established in IrisPlex, HIrisPlex and HIrisPlex-S [5-7]. Overall, I observed that all four classifiers performed almost equally well, especially for eye color. Only non-substantial differences were obtained across the different traits and across trait categories. Given this outcome, none of the ML methods applied here performed better than MLR, at least for the three traits of eye, hair, and skin color. Ultimately, due to the easier interpretability of the MLR, it is suggested at least for now and for the currently known marker sets, that the use of MLR is the most appropriate method for predicting appearance traits from DNA. Throughout my PhD project, it became apparent that the available knowledge on spatial trait prevalence values was quite restricted not only in certain appearance traits but also in continental groups. More specifically, most available and reliable data were focused on European populations and the traits that were available were mostly for eye and hair color. For other traits, such as skin color, hair structure, and freckles, the data were either extremely few or nonexistent. This was a significant obstacle throughout the project, since it prevented me from applying and testing the actual impact of the accurate trait prevalence values as priors in EVC prediction. However, the lack of data presented an opportunity to perform in-depth theoretical research, in particular testing the impact of priors within a spatial grid that included its possible values. I found out that there is a proportion of priors that showed potential to improve EVC prediction. However, caution is advised regarding misspecification of priors that can significantly deteriorate the models' performance. Furthermore, the application of different ML approaches did not show any significant improvement on the prediction performance against the standard MLR. This could be due to the nature of the traits, since some of them are multifactorial and affected by various external independent factors or due to possible limitations of the currently known predictive markers. With the available knowledge so far, it is emphasized throughout this study that for the time being, priors are refrained from being incorporated in the EVC prediction models while from the different classifiers applied, MLR is considered as the most appropriate method for EVC prediction due to its easier interpretability. In addition, the presented study highlights the importance of reference data on externally visible traits and the identification of more genetic markers that contribute to certain traits and I hope that the present work will motivate the emergence of these certain types of data collections that potentially may improve the current EVC prediction models

A Systematic Survey of Classification Algorithms for Cancer Detection

Cancer is a fatal disease induced by the occurrence of a count of inherited issues and also a count of pathological changes. Malignant cells are dangerous abnormal areas that could develop in any part of the human body, posing a life-threatening threat. To establish what treatment options are available, cancer, also referred as a tumor, should be detected early and precisely. The classification of images for cancer diagnosis is a complex mechanism that is influenced by a diverse of parameters. In recent years, artificial vision frameworks have focused attention on the classification of images as a key problem. Most people currently rely on hand-made features to demonstrate an image in a specific manner. Learning classifiers such as random forest and decision tree were used to determine a final judgment. When there are a vast number of images to consider, the difficulty occurs. Hence, in this paper, weanalyze, review, categorize, and discuss current breakthroughs in cancer detection utilizing machine learning techniques for image recognition and classification. We have reviewed the machine learning approaches like logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), decision tree (DT), and Support Vector Machines (SVM)

A review of biophysiological and biochemical indicators of stress for connected and preventive healthcare

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies

Discriminative, generative, and imitative learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (leaves 201-212).I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the Expectation-Maximization (EM) algorithm for latent discriminative learning (or latent MED).(cont.) While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior.by Tony Jebara.Ph.D

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

COST Action CA18131 Cierva Grant IJC2019-042188-I (LM-Z) Estonian Research Council grant PUT 1371The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.publishersversionpublishe