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Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

THEORETICAL AND APPLIED CLIMATOLOGY 2016年第1-2期125卷 13-25页

作者： Salcedo-Sanz, S. Deo, R. C. Carro-Calvo, L. Saavedra-Moreno, B. Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Southern Queensland Sch Agr Computat & Environm Sci Springfield Australia

Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two machine learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

关键词： air temperature Australia and New Zealand Monthly mean absolute error algorithms machine learning algorithms Values in measurement

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Improved practices in machine learning algorithms for NTL detection with imbalanced data

Improved practices in machine learning algorithms for NTL de...

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IEEE General Meeting Power& Energy Society

作者： Gerardo Figueroa Yi-Shin Chen Nelson Avila Chia-Chi Chu Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781538622131;9781538622124

Non-technical losses (NTLs) in electrical power grids, which mainly concern electrical theft, can have a major impact on the economies of energy providers and nations. The use of machine learning algorithms to detect NTLs has been widely studied to attenuate the costs of on-site inspections of electricity consumers showing suspicious consumption behavior. An issue that has not received enough attention in the research is the imbalance between fraudulent and non-fraudulent data, which can have a major negative impact on the performance of supervised learning methods. Furthermore, most methods proposed in the literature have not evaluated the effectiveness of their methodology using meaningful performance measures. We propose a framework that addresses the problem of data imbalance in supervised classification techniques for NTL detection through resampling techniques. Additionally, we present the results of our experimental evaluation using an extensive list of performance metrics, two of which have not been previously reported in the literature-the Matthews Correlation Coefficient and the F β -score. Experiments have been carried out using 22 months of electricity consumption data corresponding to over 3,400 industrial and commercial customers in Honduras. Our experimental results show that class imbalance strategies applied on supervised classifiers for NTL detection can significantly improve the quality of predictions.

关键词： Support vector machines machine learning algorithms Training Meters Loss measurement Power grids

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Intrusion detection in computer networks via machine learning algorithms

Intrusion detection in computer networks via machine learnin...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Fatih Ertam Llhan Firat Kilincer Orhan Yaman Firat University Elazig Turkey

With the internet of objects, the number of devices with internet connection is increasing day by day. This leads to a very high amount of data circulating on the internet. It is one of the most common problems that can be distinguished from normal and abnormal traffic by analyzing in high data amount. In this study, an analysis was carried out by using machine learning approaches to determine whether the data received on the internet is normal or abnormal data. In order to achieve this goal, the KDD Cup 99 data set which is frequently used in literature studies is classified by Naive Bayes (NB), bayes NET (bN), Random Forest (RF), Multilayer Perception (MLP) and Sequential Minimal Optimization (SMO) algorithms. Classifiers are also compared with false rate, precision, recall, and F measure metrics along with accuracy rate values. Classification times of classifiers are also given by comparison.

关键词： Radio frequency Computer crime Intrusion detection Niobium machine learning algorithms Measurement Probes

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A novel approach to optimization of iterative machine learning algorithms: Over heap structure

A novel approach to optimization of iterative machine learni...

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IEEE International Conference on Big Data

作者： Hasan Kurban Mehmet M. Dalkilic Department of Computer Science Indiana University Bloomington IN United States

Iterative machine learning algorithms, i.e., k-means (KM), expectation maximization (EM), become overwhelmed with big data since all data points are being continually and indiscriminately visited while a cost is being minimized. In this work, we demonstrate (1) an optimization approach to reduce training run-time complexity of iterative machine learning algorithms and (2) implementation of this framework over KM algorithm. We call this extended KM algorithm, KM*. The experimental results show that KM* outperforms KM over big real world and synthetic data sets. Lastly, we demonstrate the theoretical elements of our work.

关键词： Convergence machine learning algorithms Big Data Clustering algorithms Iterative algorithms Optimization

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Resource quality prediction based on machine learning algorithms

Resource quality prediction based on machine learning algori...

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International Conference on Systems and Informatics (ICSAI)

作者： Yu Wang Dingyu Yang Yunfan Shi Yizhen Wang Wanli Chen School of Electronics and Information Shanghai Dian Ji University Shanghai China

Many resources today are shared freely through social network or cloud storage platforms, which are helpful for uses to acquire data or exchange information. Unfortunately, due to the unrestricted participations, some resources with advertisements or fraud are also uploaded, which force users to hit the ad websites or steal users' data. Therefore, the quality evaluation of one resource is needed for users to judge whether to utilize or install it. In this paper, we implement a system to evaluate the quality based on software install packages, which applies four algorithms to forecast the quality scores. We conduct an extensive experimental study on a real dataset and find that the prediction can be performed in less than one second (0.002s~0.04s) and with a high accuracy (82.84%~90.52%).

关键词： machine learning algorithms Predictive models Data models Quality of service Standards Prediction algorithms Training

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Towards research-led teaching curriculum development for machine learning algorithms

Towards research-led teaching curriculum development for mac...

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IEEE International Conference on Teaching, Assessment and learning for Engineering (TALE)

作者： Kevin Kam Fung Yuen School of Business Singapore University of Social Sciences Singapore

Teaching and research are two essential correlated activities in tertiary education. How research can successfully be applied to benefit the teaching and learning for the students is a challenging task for curriculum development. This paper presents the real practice of research-led teaching curriculum development for machine learning algorithms. Evaluations show the proposed research-led teaching method receives very high students' satisfaction and recognition.

关键词： machine learning algorithms Curriculum development Knowledge engineering Conferences Linear regression Business

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A Conjectural Study on machine learning algorithms

A Conjectural Study on Machine Learning Algorithms

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International Conference On Soft Computing Systems (ICSCS)

作者： Sankar, Abijith Bharathi, P. Divya Midhun, M. Vijay, K. Kumar, T. Senthil Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9788132226710;9788132226697

Artificial Intelligence, a field which deals with the study and design of systems, which has the capability of observing its environment and does functionalities which aims at maximizing the probability of its success in solving problems. AI turned out to be a field which captured wide interest and attention from the scientific world, so that it gained extraordinary growth. This in turn resulted in the increased focus on a field-which deals with developing the underlying conjectures of learning aspects and learning machines-machine learning. The methodologies and objectives of machine learning played a vital role in the considerable progress gained by AI. machine learning aims at improving the learning capabilities of intelligent systems. This survey is aimed at providing a theoretical insight into the major algorithms that are used in machine learning and the basic methodology followed in them.

关键词： machine learning algorithms Supervised learning Unsupervised learning Bagging Boosting KNN Random forests Logistic regression Decision trees Naive bayes k-Means clustering Partitional clustering Divisive clustering Hierarchical clustering Agglomerative clustering

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Enhancing machine learning optimization algorithms by leveraging memory caching

Enhancing machine learning optimization algorithms by levera...

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International Conference on High Performance Computing and Simulation

作者： Imen Chakroun Tom Vander Aa Tom Ashby Exascience Life Lab. IMEC Leuven Belgium

ISBN: (纸本)9781538678800

Searching a solution space using Stochastic Gradient Descent (SGD) depends on the examples picked at each iteration of the algorithm. Therefore, best practices suggest randomizing the order of training points to visit after every epoch. This random selection is typically implemented as a random shuffling of the order of the training vectors rather than a genuine random training point selection. The shuffling is usually performed after every epoch which results in an extremely low temporal locality of access to the training set. Indeed, each training point is used once, and not before all the other training points have been visited. This means that a cache layer in the memory hierarchy of a modern HPC computer system will have little benefit for the algorithm unless all the training points fit inside that cache.

关键词： Training machine learning Optimization Convergence Computational modeling machine learning algorithms Stochastic processes Training machine learning algorithms Functional training Markov chain Computational modeling Optimization algorithms Cache memory Converge Artificial Intelligence machine learning memory hierarchy Solution space Stochastic Processes Iterative algorithms

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machine learning algorithms in context of intrusion detection

Machine learning algorithms in context of intrusion detectio...

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International Conference on Computer and Information Sciences (ICCOINS)

作者： Tahir Mehmood Helmi B. Md Rais University Teknologi Tronoh Petronas Malaysia University Teknologi Petronas Seri Iskandar Perak Malaysia

ISBN: (纸本)9781509025503

Design of efficient, accurate, and low complexity intrusion detection system is a challenging task. Intrusion detection method is a core of intrusion detection system and it can be either signature based or anomaly based. Although, signature based has high detection rate but it cannot detect novel attacks. Asymmetrically, anomaly based detection method can detect novel attacks but it has high false positive rate. Many machine learning techniques have been developed to cope with this problem. These machine learning algorithms develop a detection model in a training phase. This paper compares different supervised algorithms for the anomaly-based detection technique. The algorithms have been applied on the KDD99 dataset, which is the benchmark dataset used for anomaly-based detection technique. The result shows that not a single algorithm has a high detection rate for each class of KDD99 dataset. The performance measures used in this comparison are true positive rate, false positive rate, and precision.

关键词： machine learning algorithms Intrusion detection Classification algorithms Support vector machines Computers Decision trees Testing

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machine learning algorithms for document clustering and fraud detection

Machine learning algorithms for document clustering and frau...

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International Conference on Data Science & Engineering (ICDSE)

作者： Suresh Yaram Data Architect CSC India Limited Hyderabad India

ISBN: (纸本)9781509012824

machine learning plays very important role in processing of large amounts of structured and unstructured data. A set of algorithms can be used to get meaningful insights into the data that are helpful in making effective business decisions. Document clustering is one of the popular machine learning technique used to group unstructured data (text documents) based on its content and further analyze the data to understand the patterns in it. The unstructured data gets transformed into semi-structured data and structured data in stages by using text mining and clustering (k-means) techniques. Classification is another machine learning technique that can be implemented for use cases like "fraud detection and cross-sell & up-sell opportunity identification" in banking, financial services and insurance industry. This paper focuses on the implementation of both document clustering algorithm and a set of classification algorithms (Decision Tree, Random Forest and Naïve Bayes), along with appropriate industry use cases. Also, the performance of three classification algorithms will be compared by calculation of "Confusion Matrix" which in turn helps us to calculate performance measures such as, "accuracy", "precision", and "recall".

关键词： Classification algorithms Feature extraction Decision trees Entropy Prediction algorithms Insurance machine learning algorithms

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