The problem of indexing time series has attracted much interest. Most algorithms used to index time series utilize the Euclidean distance or some variation thereof. However, it has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic time warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis. Because of this flexibility, DTW is widely used in science, medicine, industry and finance. Unfortunately, however, DTW does not obey the triangular inequality and thus has resisted attempts at exact indexing. Instead, many researchers have introduced approximate indexing techniques or abandoned the idea of indexing and concentrated on speeding up sequential searches. In this work, we introduce a novel technique for the exact indexing of DTW. We prove that our method guarantees no false dismissals and we demonstrate its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.

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Exact indexing of dynamic time warping

Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

C4.5: Programs for Machine Learning (書評)

Publisher Summary
The indexing of very large time series databases has attracted the attention of database community in recent years. The vast majority of work in this area has focused on indexing under the Euclidean distance metric. The problem of indexing time series has attracted much research interest in the database community. Most algorithms that are used to index time series utilize the Euclidean distance or some variation thereof. However, it has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic time warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis. Because of this flexibility, DTW is widely used in science, medicine, industry, and finance. Unfortunately, however, DTW does not obey the triangular inequality and, thus, has resisted attempts at exact indexing. Instead, many researchers have introduced approximate indexing techniques, or abandoned the idea of indexing and concentrated on speeding up sequential search.

Chapter 36 – Exact Indexing of Dynamic Time Warping

Ensemble learning methods train combinations of base models, which may be decision trees, neural networks, or others traditionally used in supervised learning. Ensemble methods have gained popularity because many researchers have demonstrated their superior prediction performance relative to single models on a variety of problems especially when the correlations of the errors made by the base models are low (e.g., (Freund & Schapire 1996; Tumer & Oza 1999)). However, these learning methods have largely operated in batch mode—that is, they repeatedly process the entire set of training examples as a whole. These methods typically require at least one pass through the data for each base model in the ensemble. We would instead prefer to learn the entire ensemble in an online fashion, i.e., using only one pass through the entire dataset. This would make ensemble methods practical when data is being generated continuously so that storing data for batch learning is impractical, or in data mining tasks where the datasets are large enough that multiple passes would require a prohibitively large training time. We have so far developed online versions of the popular bagging (Breiman 1994) and boosting (Freund & Schapire 1996) algorithms. We have shown empirically that both online algorithms converge to the same prediction performance as the batch versions and proved this convergence for online bagging (Oza 2000). However, significant empirical and theoretical work remains to be done. There are several traditional ensemble learning issues that remain in our online ensemble learning framework such as the number and types of base models to use, the combining method to use, and how to maintain diversity among the base models. When learning large datasets, we may hope to avoid using all of the training examples and/or input features. We have developed input decimation (Tumer & Oza 1999), a technique that uses different subsets of the input features in different base models. We have shown that this method performs better than combinations of base models that use all the input features because of two characteristics of our base models: they overfit less by using only a small number of highly-relevant input features, and they have lower correlations in their errors because they use different input feature subsets. However, our method of selecting input features

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Online Ensemble Learning

Consistency-based diagnosis is one of the most widely used approaches to model-based diagnosis within the artificial intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, candidate generation, and candidate refinement. In that process conflict detection has proven to be a nontrivial step from the theoretical point of view. For this reason, many approaches to consistency-based diagnosis have relied upon some kind of dependency-recording. These techniques have had different problems, specially when they were applied to diagnose dynamic systems. Recently, offline dependency compilation has established itself as a suitable alternative approach to online dependency-recording. In this paper we propose the possible conflict concept as a compilation technique for consistency-based diagnosis. Each possible conflict represents a subsystem within system description containing minimal analytical redundancy and being capable to become a conflict. Moreover, the whole set of possible conflicts can be computed offline with no model evaluation. Once we have formalized the possible conflict concept, we explain how possible conflicts can be used in the consistency-based diagnosis framework, and how this concept can be easily extended to diagnose dynamic systems. Finally, we analyze its relation to conflicts in the general diagnosis engine (GDE) framework and compare possible conflicts with other compilation techniques, especially with analytical redundancy relations (ARRs) obtained through structural analysis. Based on results from these comparisons we provide additional insights in the work carried out within the BRIDGE community to provide a common framework for model-based diagnosis for both artificial intelligence and control engineering approaches.

Possible conflicts: a compilation technique for consistency-based diagnosis

CK2 represents an oncology target scientifically validated. However, clinical research with inhibitors of the CK2-mediated phosphorylation event is still insufficient to recognize it as a clinically validated target. CIGB-300, an investigational peptide-based drug that targets the phosphoaceptor site, binds to a CK2 substrate array in vitro but mainly to B23/nucleophosmin in vivo. The CIGB-300 proapoptotic effect is preceded by its nucleolar localization, inhibition of the CK2-mediated phosphorylation on B23/nucleophosmin and nucleolar disassembly. Importantly, CIGB-300 shifted a protein array linked to apoptosis, ribosome biogenesis, cell proliferation, glycolisis, and cell motility in proteomic studies which helped to understand its mechanism of action. In the clinical ground, CIGB-300 has proved to be safe and well tolerated in a First-in-Human trial in women with cervical malignancies who also experienced signs of clinical benefit. In a second Phase 1 clinical trial in women with cervical cancer stage IB2/II, the MTD and DLT have been also identified in the clinical setting. Interestingly, in cervical tumors the B23/nucleophosmin protein levels were significantly reduced after CIGB-300 treatment at the nucleus compartment. In addition, expanded use of CIGB-300 in case studies has evidenced antitumor activity when administered as compassional option. Collectively, our data outline important clues on translational and clinical research from this novel peptide-based drug reinforcing its perspectives to treat cancer and paving the way to validate CK2 as a promising target in oncology.

CIGB-300, a synthetic peptide-based drug that targets the CK2 phosphoaceptor domain. Translational and clinical research

A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduced: interval based predicates, such as always, and distance based, such as the euclidean distance. Special purpose techniques are presented that allow these predicates to be handled efficiently when performing top-down induction. Furthermore, by employing boosting, the accuracy of the resulting classifiers can be improved significantly. Experiments on several different datasets show that the proposed method is highly competitive with previous approaches.

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Learning First Order Logic Time Series Classifiers: Rules and Boosting

: Consistency-based diagnosis is the most widely used approach to model-based diagnosis within the Artificial Intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, and candidate generation and refinement. Many approaches to consistency-based diagnosis have relied on some kind of on-line dependency-recording mechanism for conflict calculation. These techniques have had different problems, specially when applied to dynamic systems. Recently, off-line compilation of dependencies has been established as a suitable alternative approach. In this work we compare one compilation technique, based on the possible conflict concept, with results obtained with the classical on-line dependency recording engine as in GDE. Moreover, we compare possible conflicts with another compilation technique coming from the FDI community, which is based on analytical redundancy relations. Finally, we study the relationship between possible conflicts, analytical redundancy relations, and conflicts.

Possible Conflicts, ARRs, and Conflicts

A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple classifiers, which consists only of one literal. The proposed predicates are based in similarity functions (i.e., euclidean and dynamic time warping) between time series.

The experimental validation of the method has been done using different datasets, some of them obtained from the UCI repositories. The results are very competitive with the reported in previous works. Moreover, their comprehensibility is better than in other approaches with similar results, since the classifiers are formed by a weighted sequence of literals.

Carlos Alonso González

Papers

Possible conflicts: a compilation technique for consistency-based diagnosis

CIGB-300, a synthetic peptide-based drug that targets the CK2 phosphoaceptor domain. Translational and clinical research

Learning First Order Logic Time Series Classifiers: Rules and Boosting

Possible Conflicts, ARRs, and Conflicts

Applying Boosting to Similarity Literals for Time Series Classification