Applying Fourier-transform infrared (FTIR) spectroscopy (or related technologies such as Raman spectroscopy) to biological questions (defined as biospectroscopy) is relatively novel. Potential fields ...of application include cytological, histological and microbial studies. This potentially provides a rapid and non-destructive approach to clinical diagnosis. Its increase in application is primarily a consequence of developing instrumentation along with computational techniques. In the coming decades, biospectroscopy is likely to become a common tool in the screening or diagnostic laboratory, or even in the general practitioner's clinic. Despite many advances in the biological application of FTIR spectroscopy, there remain challenges in sample preparation, instrumentation and data handling. We focus on the latter, where we identify in the reviewed literature, the existence of four main study goals: Pattern Finding; Biomarker Identification; Imaging; and, Diagnosis. These can be grouped into two frameworks: Exploratory; and, Diagnostic. Existing techniques in Quality Control, Pre-processing, Feature Extraction, Clustering, and Classification are critically reviewed. An aspect that is often visited is that of method choice. Based on the state-of-art, we claim that in the near future research should be focused on the challenges of dataset standardization; building information systems; development and validation of data analysis tools; and, technology transfer. A diagnostic case study using a real-world dataset is presented as an illustration. Many of the methods presented in this review are Machine Learning and Statistical techniques that are extendable to other forms of computer-based biomedical analysis, including mass spectrometry and magnetic resonance.
IRootLab is a free and open-source MATLAB toolbox for vibrational biospectroscopy (VBS) data analysis. It offers an object-oriented programming class library, graphical user interfaces (GUIs) and ...automatic MATLAB code generation. The class library contains a large number of methods, concepts and visualizations for VBS data analysis, some of which are introduced in the toolbox. The GUIs provide an interface to the class library, including a module to merge several spectral files into a dataset. Automatic code allows developers to quickly write VBS data analysis scripts and is a unique resource among tools for VBS. Documentation includes a manual, tutorials, Doxygen-generated reference and a demonstration showcase. IRootLab can handle some of the most popular file formats used in VBS. License: GNU-LGPL.
Official website: http://irootlab.googlecode.com/.
Supplementary data are available at Bioinformatics online.
Empirical Fuzzy Sets Angelov, Plamen P.; Gu, Xiaowei
International journal of intelligent systems,
February 2018, 2018-02-00, 20180201, Letnik:
33, Številka:
2
Journal Article
Recenzirano
Odprti dostop
In this paper, we introduce a new form of describing fuzzy sets (FSs) and a new form of fuzzy rule‐based (FRB) systems, namely, empirical fuzzy sets (εFSs) and empirical fuzzy rule‐based (εFRB) ...systems. Traditionally, the membership functions (MFs), which are the key mathematical representation of FSs, are designed subjectively or extracted from the data by clustering projections or defined subjectively. εFSs, on the contrary, are described by the empirically derived membership functions (εMFs). The new proposal made in this paper is based on the recently introduced Empirical Data Analytics (EDA) computational framework and is closely linked with the density of the data. This allows to keep and improve the link between the objective data and the subjective labels, linguistic terms, and classes definition. Furthermore, εFSs can deal with heterogeneous data combining categorical with continuous and/or discrete data in a natural way. εFRB systems can be extracted from data including data streams and can have dynamically evolving structure. However, they can also be used as a tool to represent expert knowledge. The main difference from the traditional FSs and FRB systems is that the expert does not need to define the MF per variable; instead, possibly multimodal, densities will be extracted automatically from the data and used as εMFs in a vector form for all numerical variables. This is done in a seamless way whereby the human involvement is only required to label the classes and linguistic terms. Moreover, even this intervention is optional. Thus, the proposed new approach to define and design the FSs and FRB systems puts the human “in the driving seat.” Instead of asking experts to define features and MFs correspondingly, to parameterize them, to define algorithm parameters, to choose types of MFs, or to label each individual item, it only requires (optionally) to select prototypes from data and (again, optionally) to label them. Numerical examples as well as a naïve empirical fuzzy (εF) classifier are presented with an illustrative purpose. Due to the very fundamental nature of the proposal, it can have a very wide area of applications resulting in a series of new algorithms such as εF classifiers, εF predictors, εF controllers, and so on. This is left for the future research.
Sense and avoid in UAS Angelov, Plamen; Angelov, Plamen
2012., 2012, 2012-04-30T00:00:00, 2012-03-06, 2012-03-16, Letnik:
61
eBook
There is increasing interest in the potential of UAV (Unmanned Aerial Vehicle) and MAV (Micro Air Vehicle) technology and their wide ranging applications including defence missions, reconnaissance ...and surveillance, border patrol, disaster zone assessment and atmospheric research. High investment levels from the military sector globally is driving research and development and increasing the viability of autonomous platforms as replacements for the remotely piloted vehicles more commonly in use.UAV/UAS pose a number of new challenges, with the autonomy and in particular collision avoidance, detect and avoid, or sense and avoid, as the most challenging one, involving both regulatory and technical issues.Sense and Avoid in UAS: Research and Applications covers the problem of detect, sense and avoid in UAS (Unmanned Aircraft Systems) in depth and combines the theoretical and application results by leading academics and researchers from industry and academia.Key features:Presents a holistic view of the sense and avoid problem in the wider application of autonomous systemsIncludes information on human factors, regulatory issues and navigation, control, aerodynamics and physics aspects of the sense and avoid problem in UASProvides professional, scientific and reliable content that is easy to understand, andIncludes contributions from leading engineers and researchers in the fieldSense and Avoid in UAS: Research and Applications is an invaluable source of original and specialised information. It acts as a reference manual for practising engineers and advanced theoretical researchers and also forms a useful resource for younger engineers and postgraduate students. With its credible sources and thorough review process, Sense and Avoid in UAS: Research and Applications provides a reliable source of information in an area that is fast expanding but scarcely covered.
Evolving fuzzy systems (EFSs) are now well developed and widely used, thanks to their ability to self-adapt both their structures and parameters online. Since the concept was first introduced two ...decades ago, many different types of EFSs have been successfully implemented. However, there are only very few works considering the stability of the EFSs, and these studies were limited to certain types of membership functions with specifically predefined parameters, which largely increases the complexity of the learning process. At the same time, stability analysis is of paramount importance for control applications and provides the theoretical guarantees for the convergence of the learning algorithms. In this paper, we introduce the stability proof of a class of EFSs based on data clouds, which are grounded at the AnYa type fuzzy systems and the recently introduced empirical data analytics (EDA) methodological framework. By employing data clouds, the class of EFSs of AnYa type considered in this paper avoids the traditional way of defining membership functions for each input variable in an explicit manner and its learning process is entirely data driven. The stability of the considered EFS of AnYa type is proven through the Lyapunov theory, and the proof of stability shows that the average identification error converges to a small neighborhood of zero. Although, the stability proof presented in this paper is specially elaborated for the considered EFS, it is also applicable to general EFSs. The proposed method is illustrated with Box-Jenkins gas furnace problem, one nonlinear system identification problem, Mackey-Glass time series prediction problem, eight real-world benchmark regression problems as well as a high-frequency trading prediction problem. Compared with other EFSs, the numerical examples show that the considered EFS in this paper provides guaranteed stability as well as a better approximation accuracy.
In this paper, a new type of multilayer rule-based classifier is proposed and applied to image classification problems. The proposed approach is entirely data-driven and fully automatic. It is ...generic and can be applied to various classification and prediction problems, but in this paper we focus on image processing, in particular. The core of the classifier is a fully interpretable, understandable, self-organized set of IF…THEN… fuzzy rules based on the prototypes autonomously identified by using a one-pass type training process. The classifier can self-evolve and be updated continuously without a full retraining. Due to the prototype-based nature, it is non-parametric; its training process is non-iterative, highly parallelizable and computationally efficient. At the same time, the proposed approach is able to achieve very high classification accuracy on various benchmark datasets surpassing most of the published methods, be comparable with the human abilities. In addition, it can start classification from the first image of each class in the same way as humans do, which makes the proposed classifier suitable for real-time applications. Numerical examples of benchmark image processing demonstrate the merits of the proposed approach.
In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses ...them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming data afterwards to follow the changing data pattern by updating the system structure and meta-parameters recursively. The meta-parameters of the proposed approach are derived from data directly. By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs. The classifier also supports different types of distance measures. Numerical examples based on benchmark datasets demonstrate the high performance of the proposed approach and its ability of handling high-dimensional, complex, large-scale problems.
Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are very powerful ...prototype-based predictive models for classification, offering both great prediction precision and high user interpretability. However, the use of zero-order FISs as base classifiers in AdaBoost has not been explored yet. To bridge the gap, in this article, a novel multiclass fuzzily weighted AdaBoost (FWAdaBoost)-based ensemble system with a self-organizing fuzzy inference system (SOFIS) as the ensemble component is proposed. To better incorporate the SOFIS, FWAdaBoost utilizes the confidence scores produced by the SOFIS in both sample weight updating and ensemble output generation, resulting in more accurate classification boundaries and greater prediction precision. Numerical examples on a wide range of benchmark classification problems demonstrate the efficacy of the proposed approach.
•A novel semi-supervised deep rule-based (SSDRB) classifier with a prototype-based nature is introduced.•The semi-supervised learning process of the SSDRB classifier is self-organising and highly ...transparent.•The SSDRB classifier is able to generate human interpretable IF...THEN... rules.•The SSDRB classifier is able to perform classification on out-of-sample images.•The SSDRB classifier outperforms the state-of-art approaches in classification accuracy.
In this paper, a semi-supervised learning approach based on a deep rule-based (DRB) classifier is introduced. With its unique prototype-based nature, the semi-supervised DRB (SSDRB) classifier is able to generate human interpretable IF...THEN...rules through the semi-supervised learning process in a self-organising and highly transparent manner. It supports online learning on a sample-by-sample basis or on a chunk-by-chunk basis. It is also able to perform classification on out-of-sample images. Moreover, the SSDRB classifier can learn new classes from unlabelled images in an active way becoming dynamically self-evolving. Numerical examples based on large-scale benchmark image sets demonstrate the strong performance of the proposed SSDRB classifier as well as its distinctive features compared with the “state-of-the-art” approaches.
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