24:I[63208,["8920","static/chunks/8920-efbff8517d590b7e.js","9097","static/chunks/9097-7e60ff382fede0de.js","5914","static/chunks/5914-356082358a498a0b.js","9903","static/chunks/9903-11222066602aad8e.js","5438","static/chunks/5438-cfc6cd3ec63ed58c.js","3851","static/chunks/3851-d9d99b9df58ccefb.js","4621","static/chunks/4621-60797c2425663d2a.js","6871","static/chunks/6871-85cb03c97585f46a.js","6736","static/chunks/6736-932312a004c99faf.js","4550","static/chunks/4550-0bf059e3050c08b3.js","688","static/chunks/688-a6a2ac7bff269cd3.js","3984","static/chunks/3984-063c0b62f32133c7.js","7038","static/chunks/7038-4f87670d4e55ef18.js","9039","static/chunks/9039-75366c62dc7d0d0e.js","4563","static/chunks/4563-ad980d1babbf7b0b.js","6761","static/chunks/6761-0c10d1446de7ae1c.js","744","static/chunks/744-1e76229abb9e297b.js","7342","static/chunks/7342-1586df78600bf911.js","3617","static/chunks/3617-1f62a097637f4747.js","5895","static/chunks/5895-caa6c774914eb108.js","7481","static/chunks/7481-3b84c503a2ebfb3f.js","3668","static/chunks/3668-7dab69e4121c33af.js","39","static/chunks/39-e93a27d0920e6d92.js","2881","static/chunks/2881-445cc2384af2c6f9.js","2364","static/chunks/2364-e83d52fb21729d99.js","3208","static/chunks/3208-e65462d166bd3688.js","7582","static/chunks/app/toc/%5Btla%5D/%5Bvolume%5D/%5Bissue%5D/page-694a0e465c2c1334.js"],"default"] 25:T504,

Time series classification is a research area which has drawn much attention over the past decade. A novel approach for classification of time series uses shapelets. A shapelet is a subsequence extracted from one of the time series in the dataset which best separates between time series coming from different classes of the data set. A disadvantage of current shapelet-based classification approaches is their high time and memory consumption, which results from the examination of all possible subsequences.

In this study, our initial goal was to find an evaluation order of the shapelets space which enables fast generation of an accurate classification model with a small memory footprint. The comparative analysis we conducted clearly indicates that a random evaluation order yields the best results.

We present an algorithm for randomized model generation for shapelet-based classification that can generate a model with surprisingly high accuracy after evaluating only an exceedingly small fraction (∼ 10^-3) of the shapelets space and has modest memory requirements. We propose several methods for estimating the number of shapelets to examine, and present extensive evaluation on 51 data sets establishing the effectiveness of our approach.

26:T52d,

Association rule mining that mainly focuses on symbolic items presented in transactions has attracted considerable interest since a rule provides a concise and intuitive description of knowledge. However, a time series is a sequence of data that is typically recorded in temporal order at fixed intervals of time. In order to mining rules in the context of time series data, a symbolic aggregate approximation (SAX) representation that could discretize the real-valued and high-dimensional time series data into segments and convert each segment to a symbol is applied in this paper. On this basis, a modified CBA algorithm is proposed to discover Class Sequential Rules (CSRs) and make the final prediction at first. Then we propose a new lazy associative classification method, in which the computation is performed on a demand driven basis. This is in contrast to rule-based classification methods like CBA which generate excessive number of rules, but is still unable to cover some test data with the discovered rules. Various experimental results show that our lazy associative classification for time series can be interpretable and competitive with the current state-of-the-art algorithm. In addition, four different methods that select the mined CSR(s) are proposed for carrying out associative classification.

27:T407,

Some of the basic algorithms for learning the structure of Bayesian networks, such as the well-known K2 a