Machine learning: a review of classification and combining techniques

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.     

Machine learning: a survey of current techniques

Machine learning is the essence of machine intelligence. When we have systems that learn, we will have true artificial intelligence. Many machine-learning strategies exist, this paper reviews the state of the art in machine learning and provides a glimpse of the pioneers of present machine-learning systems and strategies. Learning in noisy domains, the evolutionary learning, learning by analogy and explanation-based learning are just some of the methods covered. Emphasis is placed on the algorithms employed by many of the systems, and the merits and disadvantages of various approaches. Finally an examination of VanLehn's theory of impasse-driven learning is made.     

Deep learning techniques for tumor segmentation: a review

Recently, deep learning, especially convolutional neural networks, has achieved the remarkable results in natural image classification and segmentation. At the same time, in the field of medical image segmentation, researchers use deep learning techniques for tasks such as tumor segmentation, cell segmentation, and organ segmentation. Automatic tumor segmentation plays an important role in radiotherapy and clinical practice and is the basis for the implementation of follow-up treatment programs. This paper reviews the tumor segmentation methods based on deep learning in recent years. We first introduce the common medical image types and the evaluation criteria of segmentation results in tumor segmentation. Then, we review the tumor segmentation methods based on deep learning from technique view and tumor view, respectively. The technique view reviews the researches from the architecture of the deep learning and the tumor view reviews from the type of tumors.

     

Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

The current review contributes with an extensive overview of decision support systems in diagnosing heart diseases in clinical settings. The investigators independently screened and abstracted studies related to heart diseases-based clinical decision support system (DSS) published until 8-June-2015 in PubMed, CINAHL and Cochrane Library. The data extracted from the twenty full-text articles that met the inclusion criteria was classified under the following fields; heart diseases, methods for data sets formation, machine learning algorithms, machine learning-based DSS, comparator types, outcome evaluation and clinical implications of the reported DSS. Out of total of 331 studies 20 met the inclusion criteria. Most of the studies relate to ischemic heart diseases with neural network being the most common machine learning (ML) technique. Among the ML techniques, ANN classifies myocardial infarction with 97% and myocardial perfusion scintigraphy with 87.5% accuracy, CART classifies heart failure with 87.6%, neural network ensembles classifies heart valve with 97.4%, support vector machine classifies arrhythmia screening with 95.6%, logistic regression classifies acute coronary syndrome with 72%, artificial immune recognition system classifies coronary artery disease with 92.5% and genetic algorithms and multi-criteria decision analysis classifies chest-pain patients with 91% accuracy respectively. There were 55% studies that validated the results in clinical settings while 25% validated the results through experimental setups. Rest of the studies (20%) did not report the applicability and feasibility of their methods in clinical settings. The study categorizes the ML techniques according to their performance in diagnosing various heart diseases. It categorizes, compares and evaluates the comparator based on physician’s performance, gold standards, other ML techniques, different models of same ML technique and studies with no comparison. It also investigates the current, future and no clinical implications. In addition, trends of machine learning techniques and algorithms used in the diagnosis of heart diseases along with the identification of research gaps are reported in this study. The reported results suggest reliable interpretations and detailed graphical self-explanatory representations by DSS. The study reveals the need for establishment of non-ambiguous real-time clinical data for proper training of DSS before it can be used in clinical settings. The future research directions of the ML-based DSS is mostly directed towards development of generalized systems that can decide on clinical measurements which are easily accessible and assessable in real-time.     

Machine learning for intrusion detection in MANET: a state-of-the-art survey

Machine learning consists of algorithms that are first trained with reference input to “learn” its specifics and then used on unseen input for classification purposes. Mobile ad-hoc wireless networks (MANETs) have drawn much attention to research community due to their advantages and growing demand. However, they appear to be more susceptible to various attacks harming their performance than any other kind of network. Intrusion Detection Systems represent the second line of defense against malevolent behavior to MANETs, since they monitor network activities in order to detect any malicious attempt performed by intruders. Due to the inherent distributed architecture of MANET, traditional cryptography schemes cannot completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying machine learning methods for IDS these challenges can be overcome. In this paper, we present the most prominent models for building intrusion detection systems by incorporating machine learning in the MANET scenario. We have structured our survey into four directions of machine learning methods: classification approaches, association rule mining techniques, neural networks and instance based learning approaches. We analyze the most well-known approaches and present notable achievements but also drawbacks or flaws that these methods have. Finally, in concluding our survey we provide some findings of paramount importance identifying open issues in the MANET field of interest.     

A systematic review of machine learning techniques for stance detection and its applications

Neural Computing and Applications - Stance detection is an evolving opinion mining research area motivated by the vast increase in the variety and volume of user-generated content. In this regard,...     

Machine learning and deep learning based predictive quality in manufacturing: a systematic review

Journal of Intelligent Manufacturing - With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements,...     

TINYCD: a (not so) deep learning model for change detection

Neural Computing and Applications - In this paper, we present a lightweight and effective change detection model, called TinyCD. This model has been designed to be faster and smaller than current...     

The use of artificial intelligence based techniques for intrusion detection: a review

The Internet connects hundreds of millions of computers across the world running on multiple hardware and software platforms providing communication and commercial services. However, this interconnectivity among computers also enables malicious users to misuse resources and mount Internet attacks. The continuously growing Internet attacks pose severe challenges to develop a flexible, adaptive security oriented methods. Intrusion detection system (IDS) is one of most important component being used to detect the Internet attacks. In literature, different techniques from various disciplines have been utilized to develop efficient IDS. Artificial intelligence (AI) based techniques plays prominent role in development of IDS and has many benefits over other techniques. However, there is no comprehensive review of AI based techniques to examine and understand the current status of these techniques to solve the intrusion detection problems. In this paper, various AI based techniques have been reviewed focusing on development of IDS. Related studies have been compared by their source of audit data, processing criteria, technique used, dataset, classifier design, feature reduction technique employed and other experimental environment setup. Benefits and limitations of AI based techniques have been discussed. The paper will help the better understanding of different directions in which research has been done in the field of IDS. The findings of this paper provide useful insights into literature and are beneficial for those who are interested in applications of AI based techniques to IDS and related fields. The review also provides the future directions of the research in this area.     

Machine learning for landslides prevention: a survey

Neural Computing and Applications - Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic...     

Machine learning in digital games: a survey

Artificial intelligence for digital games constitutes the implementation of a set of algorithms and techniques from both traditional and modern artificial intelligence in order to provide solutions to a range of game dependent problems. However, the majority of current approaches lead to predefined, static and predictable game agent responses, with no ability to adjust during game-play to the behaviour or playing style of the player. Machine learning techniques provide a way to improve the behavioural dynamics of computer controlled game agents by facilitating the automated generation and selection of behaviours, thus enhancing the capabilities of digital game artificial intelligence and providing the opportunity to create more engaging and entertaining game-play experiences. This paper provides a survey of the current state of academic machine learning research for digital game environments, with respect to the use of techniques from neural networks, evolutionary computation and reinforcement learning for game agent control.     

Tools,techniques, datasets and application areas for object detection in an image: a review

Multimedia Tools and Applications - Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do...     

Machine learning for targeted display advertising: transfer learning in action

This paper presents the design of a fully deployed multistage transfer learning system for targeted display advertising, highlighting the important role of problem formulation and the sampling of data from distributions different from that of the target environment. Notably, the machine learning system itself is deployed and has been in continual use for years for thousands of advertising campaigns—in contrast to the more common case where predictive models are built outside the system, curated, and then deployed. In this domain, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate distributions and learning tasks, and then transferred to the target task. We present the design of the transfer learning system We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We also present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from over half a decade of research and development on this complex, deployed, and intensely used machine learning system.     

Machine learning in information systems - a bibliographic review and open research issues

Electronic Markets - Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant...     

Machine learning in biology: a profile of David Haussler

This paper helped initiate the integration of a branch of machine learning theory with machine learning practice, according to Carnegie Mellon's Tom Mitchell, a world-renowned machine learning researcher and past president of the AAAI. Haussler's paper helped introduce the AI community to PAC (probably approximately correct) learning - then a new line of theoretical work - and linked it with ongoing AI work in machine learning.     

无线传感网离群点检测技术研究综述

无线传感器网络(wireless sensor network,WSN)受电池能量、计算能力、通信能力和内存空间及传感数据多维特征的限制,传统的离群点检测技术不能直接应用于WSN,因此出现了一系列针对WSN的离群点检测技术.对已有的WSN离群点检测技术进行了概述,根据各离群点检测技术的特征进行了分类和分析,并结合现有技术的缺陷和需求,展望了WSN离群点检测技术的未来研究方向和目标.     

Some learning techniques in hierarchical censored production rules (HCPRs) system

This paper discusses learning techniques based upon the hierarchical censored production rules (HCPRs) system of knowledge representation. These HCPRs are written in the form: “A IF B UNLESS C GENERALITY G SPECIFICITY S,” where symbol A represents the conclusion, B is the set of preconditions, C is the set of exception conditions, G is the general information, while S represents the specific information. Learning can be classified into two major categories: the first includes the restructuring or modification of existing knowledge, and the second covers the creation of new knowledge depending upon externally supplied information and already acquired knowledge. In this system, schemes which modify various belief factors and information relegated to various operators (like IF, UNLESS, etc.) of an HCPR fall in the first category, while schemes which create a new HCPR in the system by using externally supplied information and already acquired knowledge fall in the second category. Using the growth algorithm, a new HCPR is added in the system by maintaining consistency as well as minimizing redundancy. The set of all related HCPRs connected to the SPECIFICITY or GENERALITY operators are shown to possess a tree structure, and hence it is given the name HCPRs tree. The fission algorithm restructures an HCPRs tree, thereby enabling the system to reorganize its knowledge base; a new HCPR may be created during this process. This is followed by the fusion algorithm that enables the merging of two related HCPRs trees in the HCPRs system. © 1998 John Wiley & Sons, Inc.     

Active learning for on-road vehicle detection: a comparative study

In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have been performed to comparatively assess costs and merits. In this study, we provide a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection. The generality of active learning findings is demonstrated via learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG–SVM), and Haar-like features and Adaboost classification (Haar–Adaboost). Experimental evaluation has been performed on static images and real-world on-road vehicle datasets. Learning approaches are assessed in terms of the time spent annotating, data required, recall, and precision.     

Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems

In this paper, we present a fast learning fully complex-valued extreme learning machine classifier, referred to as ‘Circular Complex-valued Extreme Learning Machine (CC-ELM)’ for handling real-valued classification problems. CC-ELM is a single hidden layer network with non-linear input and hidden layers and a linear output layer. A circular transformation with a translational/rotational bias term that performs a one-to-one transformation of real-valued features to the complex plane is used as an activation function for the input neurons. The neurons in the hidden layer employ a fully complex-valued Gaussian-like (‘sech’) activation function. The input parameters of CC-ELM are chosen randomly and the output weights are computed analytically. This paper also presents an analytical proof to show that the decision boundaries of a single complex-valued neuron at the hidden and output layers of CC-ELM consist of two hyper-surfaces that intersect orthogonally. These orthogonal boundaries and the input circular transformation help CC-ELM to perform real-valued classification tasks efficiently.Performance of CC-ELM is evaluated using a set of benchmark real-valued classification problems from the University of California, Irvine machine learning repository. Finally, the performance of CC-ELM is compared with existing methods on two practical problems, viz., the acoustic emission signal classification problem and a mammogram classification problem. These study results show that CC-ELM performs better than other existing (both) real-valued and complex-valued classifiers, especially when the data sets are highly unbalanced.