Browsing by Author "Lee, Yuh-Jye"

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A study on anomaly detection ensembles
(2017-05-01)
Chiang, Alvin
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David, Esther
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Lee, Yuh-Jye
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Leshem, Guy
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Yeh, Yi-Ren
An anomaly, or outlier, is an object exhibiting differences that suggest it belongs to an as-yet undefined class or category. Early detection of anomalies often proves of great importance because they may correspond to events such as fraud, spam, or device malfunctions. By automating the creation of a ranking or list of deviations, we can save time and decrease the cognitive overload of the individuals or groups responsible for responding to such events. Over the years many anomaly and outlier metrics have been developed. In this paper we propose a clustering-based score ensembling method for outlier detection. Using benchmark datasets we evaluate quantitatively the robustness and accuracy of different ensemble strategies. We find that ensembling strategies offer only limited value for increasing overall performance, but provide robustness by negating the influence of severely underperforming models. (C) 2017 Elsevier B.V. All rights reserved.
Big Active Learning
(2017-01-01)
Huang, Er-Chen
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Pao, Hsing-Kuo
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Lee, Yuh-Jye
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應用數學系
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Department of Applied Mathematics
Active learning is a common strategy to deal with large-scale data with limited labeling effort. In each iteration of active learning, a query is ready for oracle to answer such as what the label is for a given unlabeled data. Given the method, we can request the labels only for those data that are essential and save the labeling effort from oracle. We focus on pool-based active learning where a set of unlabeled data is selected for querying in each run of active learning. To apply pool-based active learning to massive high-dimensional data, especially when the unlabeled data set is much larger than the labeled set, we propose the APRAL and MLP strategies so that the computation for active learning can be dramatically reduced while keeping the model power more or less the same. In APRAL, we avoid unnecessary data re-ranking given an unlabeled data selection criteria. To further improve the efficiency, with MLP, we organize the unlabeled data in a multi-layer pool based on a dimensionality reduction technique and the most valuable data to know their label information are more likely to store in the top layers. Given the APRAL and MLP strategies, the active learning computation time is reduced by about 83% if compared to the traditional active learning ones; at the same time, the model effectiveness remains.
Compressed Learning for Time Series Classification
(2016-01-01)
Lee, Yuh-Jye
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Pao, Hsing-Kuo
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Shih, Shueh-Han
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Lin, Jing-Yao
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Chen, Xin-Rong
The time series classification has been studied for various applications in the last decades. In the time series classification problem, we decide the class information based on a small piece of the time series inputs. In general, the approaches to time series classification can be categorized into three types, distance-based, model-based, and feature-based approaches. In this research, we focus on the feature-based methods, which represent time series as a set of characterized values. It is quite often the case that features generated by existing representation techniques are not transparent to domain experts and the feature that are selected for classification are not completely interpretable. We aim to propose a novel time series representation, called Envelope to solve the problem. The proposed supervised feature extraction method transforms time series into simple 1/0/-1 values. A heuristic is introduced to determine the most appropriate representation which includes the features that are the best to discriminate data of different labels. Moreover, this new representation enjoys the characteristic of sparsity which is an essential property when we need to apply compressed sensing techniques. With this advantage, we can benefit from high transmission efficiency, the reduction of required storage and model complexity. We conduct a series of tests on various benchmark time series data to show the effectiveness of the proposed method. Other than the classification effectiveness, we demonstrate how to visualize the similarity between time series of the same and different kinds from the proposed Envelope method.
Dealing with Interleaved Event Inputs for Intrusion Detection
(2019-01-01)
Pao, Hsing-Kuo
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Lee, Fong-Ruei
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Lee, Yuh-Jye
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應用數學系
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Department of Applied Mathematics
We propose an intrusion detection method that can deal with interleaved event inputs. The event sequences may be alert sequences in a network or running processes on a host which are both considered to contain mixed behaviors with unpredictable orders in the temporal domain. To detect intrusions with interleaved event sequences, one of the major difficulties is to separate the interleaved events that are produced by different users or for different intentions. We propose a novel ATM algorithm to extract subsequences that characterize different behaviors; afterwards, a method that is based on graph representation is used to detect intrusions. In a network, there could be intruders who plan a DDoS attack on an environment that has mostly benign users. The proposed method can distinguish between different pieces of network data that represent different behaviors and locate where the intrusion is. On a host, users without enough privilege may inappropriately gain access to data that they are not supposed to see. The proposed method can detect the event subsequence that is associated with the unauthorized activity given a usage sequence from users such as process, command or log sequences. Given the network or host-based data, the experiment results show that the proposed method can reach high precision and recall rates at the same time in the intrusion detection task. Moreover, the graphs produced by the proposed ATM method are also compared to the graphs generated from other methods to confirm that the ATM-based graph representation indeed describes meaningful transitions between events.
Multiclass support vector classification via coding and regression
(2010-03-01)
Chen, Pei-Chun
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Lee, Kuang-Yao
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Lee, Tsung-Ju
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Lee, Yuh-Jye
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Huang, Su-Yun
The multiclass classification problem is considered and resolved through coding and regression. There are various coding schemes for transforming class labels into response scores. An equivalence notion of coding schemes is developed, and the regression approach is adopted for extracting a low-dimensional discriminant feature subspace. This feature subspace can be a linear subspace of the column span of original input data or kernel-mapped feature data. The classification training and prediction are carried out in this feature subspace using a linear classifier, which lead to a simple and computationally light but yet powerful toolkit for classification. Experimental results, including prediction ability and CPU time comparison with LIBSVM, show that the regression-based approach is a competent alternative for the multiclass problem. (C) 2009 Elsevier B.V. All rights reserved.
Sparse Sampling for Sensing Temporal Data - Building an Optimized Envelope
(2016-01-01)
Domb, Menachem
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Leshem, Guy
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Bonchek-Dokow, Elisheva
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David, Esther
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Lee, Yuh-Jye
IoT systems collect vast amounts of data which can be used in order to track and analyze the structure of future recorded data. However, due to limited computational power, bandwith, and storage capabilities, this data cannot be stored as is, but rather must be reduced in such a way so that the abilities to analyze future data, based on past data, will not be compromised. We propose a parameterized method of sampling the data in an optimal way. Our method has three parameters-an averaging method for constructing an average data cycle from past observations, an envelope method for defining an interval around the average data cycle, and an entropy method for comparing new data cycles to the constructed envelope. These parameters can be adjusted according to the nature of the data, in order to find the optimal representation for classifying new cycles as well as for identifying anomalies and predicting future cycle behavior. In this work we concentrate on finding the optimal envelope, given an averaging method and an entropy method. We demonstrate with a case study of meteorological data regarding El Ninio years.
Special Issue on Technologies and Applications of Artificial Intelligence Foreword
(2012-01-01)
Hsu, Chun-Nan
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Hsu, Tsan-sheng
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King, Irwing
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Lee, Yuh-Jye
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Wu, I-Chen
Two smooth support vector machines for epsilon-insensitive regression
(2018-05-01)
Gu, Weizhe
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Chen, Wei-Po
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Ko, Chun-Hsu
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Lee, Yuh-Jye
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Chen, Jein-Shan
In this paper, we propose two new smooth support vector machines for -insensitive regression. According to these two smooth support vector machines, we construct two systems of smooth equations based on two novel families of smoothing functions, from which we seek the solution to -support vector regression (-SVR). More specifically, using the proposed smoothing functions, we employ the smoothing Newton method to solve the systems of smooth equations. The algorithm is shown to be globally and quadratically convergent without any additional conditions. Numerical comparisons among different values of parameter are also reported.