Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance

[[abstract]]Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can obtain lower classification error rates than a conventional batch training approach. Yet the reason for that is still not very clear. In this study, the feasibility of IAL is verified by mathematical approaches. Moreover, experimental results derived by IAL neural networks on benchmarks also confirm the mathematical validation.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]電子

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

Statistical feature ordering for neural-based incremental attribute learning

In pattern recognition, better classification or regression results usually depend on highly discriminative features (also known as attributes) of datasets. Machine learning plays a significant role in the performance improvement for classification and regression. Different from the conventional machine learning approaches which train all features in one batch by some predictive algorithms like neural networks and genetic algorithms, Incremental Attribute Learning (IAL) is a novel supervised machine learning approach which gradually trains one or more features step by step. Such a strategy enables features with greater discrimination abilities to be trained in an earlier step, and avoids interference among relevant features. Previous studies have confirmed that IAL is able to generate accurate results with lower error rates. If features with different discrimination abilities are sorted in different training order, the final results may be strongly influenced. Therefore, the way to sequentially sort features with some orderings and simultaneously reduce the pattern recognition error rates based on IAL inevitably becomes an important issue in this study. Compared with the applicable yet time-consuming contribution-based feature ordering methods which were derived in previous studies, more efficient feature ordering approaches for IAL are presented to tackle classification problems in this study. In the first approach, feature orderings are calculated by statistical correlations between input and output. The second approach is based on mutual information, which employs minimal-redundancy-maximal- relevance criterion (mRMR), a well-known feature selection method, for feature ordering. The third method is improved by Fisher's Linear Discriminant (FLD). Firstly, Single Discriminability (SD) of features is presented based on FLD, which can cope with both univariate and multivariate output classification problems. Secondly, a new feature ordering metric called Accumulative Discriminability (AD) is developed based on SD. This metric is designed for IAL classification with dynamic feature dimensions. It computes the multidimensional feature discrimination ability in each step for all imported features including those imported in previous steps during the IAL training. AD can be treated as a metric for accumulative effect, while SD only measures the one-dimensional feature discrimination ability in each step. Experimental results show that all these three approaches can exhibit better performance than the conventional one-batch training method. Furthermore, the results of AD are the best of the three, because AD is much fitter for the properties of IAL, where feature number in IAL is increasing. Moreover, studies on the combination use of feature ordering and selection in IAL is also presented in this thesis. As a pre-process of machine learning for pattern recognition, sometimes feature orderings are inevitably employed together with feature selection. Experimental results show that at times these integrated approaches can obtain a better performance than non-integrated approaches yet sometimes not. Additionally, feature ordering approaches for solving regression problems are also demonstrated in this study. Experimental results show that a proper feature ordering is also one of the key elements to enhance the accuracy of the results obtained