Highly comparative feature-based time-series classification

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation

Randomizing Ensemble-based approaches for Outlier

The data size is increasing dramatically every day, therefore, it has emerged the need of detecting abnormal behaviors, which can harm seriously our systems. Outlier detection refers to the process of identifying outlying activities, which diverge from the remaining group of data. This process, an integral part of data mining field, has experienced recently a substantial interest from the data mining community. An outlying activity or an outlier refers to a data point, which significantly deviates and appears to be inconsistent compared to other data members. Ensemble-based outlier detection is a line of research employed in order to reduce the model dependence from datasets or data locality by raising the robustness of the data mining procedures. The key principle of an ensemble approach is using the combination of individual detection results, which do not contain the same list of outliers in order to come up with a consensus finding. In this paper, we propose a novel strategy of constructing randomized ensemble outlier detection. This approach is an extension of the heuristic greedy ensemble construction previously built by the research community. We will focus on the core components of constructing an ensemble –based algorithm for outlier detection. The randomization will be performed by intervening into the pseudo code of greedy ensemble and implementing randomization in the respective java code through the ELKI data-mining platform. The key purpose of our approach is to improve the greedy ensemble and to overcome its local maxima problem. In order to induce diversity, it is performed randomization by initializing the search with a random outlier detector from the pool of detectors. Finally, the paper provides strong insights regarding the ongoing work of our randomized ensemble-based approach for outlier detection. Empirical results indicate that due to inducing diversity by employing various outlier detection algorithms, the randomized ensemble approach performs better than using only one outlier detector

Scalable And Efficient Outlier Detection In Large Distributed Data Sets With Mixed-type Attributes

An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding if a data point is an outlier is often some measure or notion of dissimilarity between the data point under consideration and the rest. Traditional outlier detection methods assume numerical or ordinal data, and compute pair-wise distances between data points. However, the notion of distance or similarity for categorical data is more difficult to define. Moreover, the size of currently available data sets dictates the need for fast and scalable outlier detection methods, thus precluding distance computations. Additionally, these methods must be applicable to data which might be distributed among different locations. In this work, we propose novel strategies to efficiently deal with large distributed data containing mixed-type attributes. Specifically, we first propose a fast and scalable algorithm for categorical data (AVF), and its parallel version based on MapReduce (MR-AVF). We extend AVF and introduce a fast outlier detection algorithm for large distributed data with mixed-type attributes (ODMAD). Finally, we modify ODMAD in order to deal with very high-dimensional categorical data. Experiments with large real-world and synthetic data show that the proposed methods exhibit large performance gains and high scalability compared to the state-of-the-art, while achieving similar accuracy detection rates