22,872 research outputs found
Global and decomposition evolutionary support vector machine approaches for time series forecasting
Multi-step ahead Time Series Forecasting (TSF) is a key tool for support- ing tactical decisions (e.g., planning resources). Recently, the support vector machine emerged as a natural solution for TSF due to its nonlinear learning capabilities. This paper presents two novel Evolutionary Support Vector Machine (ESVM) methods for multi-step TSF. Both methods are based on an Estimation Distribution Algorithm (EDA) search engine that automatically performs a simultaneous variable (number of inputs) and model (hyperparameters) selection. The Global ESVM (GESVM) uses all past patterns to fit the support vector machine, while the Decomposition ESVM (DESVM) separates the series into trended and stationary effects, using a distinct ESVM to forecast each effect and then summing both predictions into a sin- gle response. Several experiments were held, using six time series. The proposed approaches were analyzed under two criteria and compared against a recent Evolu- tionary Artificial Neural Network (EANN) and two classical forecasting methods, Holt-Winters and ARIMA. Overall, the DESVM and GESVM obtained competitive and high quality results. Furthermore, both ESVM approaches consume much less computational effort when compared with EANN.The authors wish to thank Ramon Sagarna for introducing the subject of EDA. The work of P. Cortez was supported by FEDER (program COMPETE and FCT) under project FCOMP-01-0124-FEDER-022674
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and
Practice 2017 worksho
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
It is important to identify the change point of a system's health status,
which usually signifies an incipient fault under development. The One-Class
Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly
detection and hence could be used for identifying change points; however, it is
sometimes difficult to obtain a good OC-SVM model that can be used on sensor
measurement time series to identify the change points in system health status.
In this paper, we propose a novel approach for calibrating OC-SVM models. The
approach uses a heuristic search method to find a good set of input data and
hyperparameters that yield a well-performing model. Our results on the C-MAPSS
dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in
detecting change point in time series with fewer training data, compared to
state-of-the-art deep learning approaches. In our case study, the OC-SVM
calibrated by the proposed model is shown to be useful especially in scenarios
with limited amount of training data
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Temporal difference learning with interpolated table value functions
This paper introduces a novel function approximation architecture especially well suited to temporal difference learning. The architecture is based on using sets of interpolated table look-up functions. These offer rapid and stable learning, and are efficient when the number of inputs is small. An empirical investigation is conducted to test their performance on a supervised learning task, and on themountain car problem, a standard reinforcement learning benchmark. In each case, the interpolated table functions offer competitive performance. ©2009 IEEE
Investigating learning rates for evolution and temporal difference learning
Evidently, any learning algorithm can only learn on the basis of the information given to it. This paper presents a first attempt to place an upper bound on the information rates attainable with standard co-evolution and with TDL. The upper bound for TDL is shown to be much higher than for coevolution. Under commonly used settings for learning to play Othello for example, TDL may have an upper bound that is hundreds or even thousands of times higher than that of coevolution. To test how well these bounds correlate with actual learning rates, a simple two-player game called Treasure Hunt. is developed. While the upper bounds cannot be used to predict the number of games required to learn the optimal policy, they do correctly predict the rank order of the number of games required by each algorithm. © 2008 IEEE
Pairwise meta-rules for better meta-learning-based algorithm ranking
In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset
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