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In recent years plenty of new algorithms for data stream classification were developed. The occurrence of different concept drift types in data streams turned out to be especially challenging. Much attention was paid to the ensemble methods because of their desired properties. However, the problem of deciding how many components should be stored in the ensemble is still an open issue. Therefore in...
This study brings together systematised views of two related areas: data editing for the nearest neighbour classifier and adaptive learning in the presence of concept drift. The growing number of studies in the intersection of these areas warrants a closer look. We revise and update the taxonomies of the two areas proposed in the literature and argue that they are not sufficiently discriminative with...
By further extending SpikeProp, we propose a backpropagation learning algorithm, which adjusts all the parameters, synaptic weights, synaptic delays, synaptic time constants, and neurons' thresholds, for spiking neural networks with multiple layers and multiple spiking neurons.
Many classification problems involve instances that are unlabeled, multi-view and multi-class. However, few technique has been benchmarked for this complex scenario, with a notable exception that combines co-trained naive bayes (CoT-NB) with BCH coding. In this paper, we benchmark the performance of co-regularized least square regression (CoR-LS) for semi-supervised multi-view multi-class classification...
Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of...
It is well-known that recommendation system which is widely used in many e-commerce platforms to recommend items to the right users suffers from data sparsity, imbalanced rating and cold start problems. Matrix factorization is a good way to deal with the sparsity and imbalance problems, which is however unable to make prediction for new users due to the lack of auxiliary information. With the advent...
In this paper, we propose a novel approach for detecting multiple changes from two multi-temporal images. Despite the development of the change vector analysis (CVA) framework and its improved version the compressed CVA (C2VA) framework, it is found that they are limited when tackling the multi-change detection task for the images with one channel. Also, the intensity itself is fragile due to the...
Imbuing neural networks with memory and attention mechanisms allows for better generalisation with fewer data samples. By focusing only on the relevant parts of data, which is encoded in an internal “memory” format, the network is able to infer better and more reliable patterns. Most neuronal attention mechanisms are based on internal networks structures that impose a similarity metric (e.g., dot-product),...
In this paper, we propose to deal with the problems of logistic regression with outliers and class imbalance, which are common in a wide range of practical applications. The robust bounded logistic regression with different error costs is developed to reduce the combined influence of outliers and class imbalance. First, inspired by the Correntropy induced loss function, we develop the bounded logistic...
Many works have attempted to characterize the complexity of classification problems by measures extracted from their learning datasets. These indexes provide indicatives of the inherent difficulty in solving a given classification problem. Although regression problems are equally frequent, there is a lack of studies in Machine Learning dedicated to understanding their complexity. This paper proposes...
The challenging problem of forecasting a given time series as accurately as possible is reality in different areas of expertise. The requirement of achieving reliable forecasts, for assisting the new generation of soft sensors, requests the development of novel smart mechanisms to be integrated into the available forecasting models. This current paper improves the previous work of Coelho et al. [1],...
Deep learning has recently gained popularity in many machine learning applications, but a theoretical grounding for the strengths, weaknesses, and implicit biases of various deep learning methods is still a work in progress. Here, we analyze the role of spatial locality in Deep Belief Networks (DBN) and show that spatially local information is lost through diffusion as the network becomes deeper....
With the massive data challenges nowadays and the rapid growing of technology, stream mining has recently received considerable attention. To address the large number of scenarios in which this phenomenon manifests itself suitable tools are required in various research fields. Instance-based data stream algorithms generally employ the Euclidean distance for the classification task underlying this...
Changes in the data distribution (concept drift) makes online learning a challenge that is progressively attracting more attention. This paper proposes Boosting-like Online Learning Ensemble (BOLE) based on heuristic modifications to Adaptable Diversity-based Online Boosting (ADOB), which is a modified version of Oza and Russell's Online Boosting. More precisely, we empirically investigate the effects...
Kernel k-means is seen as a non-linear extension of the k-means clustering method, with good performance in identifying non-isotropic and linearly inseparable clusters. However space and time requirement of kernel k-means is expensive with O(n2) complexity. Present applications with large in-memory computations make this method insuitable for large data sets. Recently, a simple prototype based hybrid...
This paper deals with the prediction of photoplethysmography (PPG) signal which is chaotic in nature. A sequential variant of the extreme learning machine (ELM) is shown to yield satisfactory prediction performance as per the calculated root-meansquare error (RMSE). Moreover, as a second measure of goodness, the heart rate determined from the predicted PPG signal is shown to be within limits of agreement...
In this paper, an EEG-based brain-computer interface (BCI) system used for emotion recognition is proposed to detect two basic emotional states (happiness and sadness). Selection of frequency bands plays a vital role in distinguishing brain patterns associated with emotions. This paper explores a new method to select suitable subject-specific frequency bands instead of using fixed frequency bands...
We study the problem of learning lexicographic preferences on multiattribute domains, and propose Rankdom Forests as a compact way to express preferences in learning to rank scenarios. We start generalizing Conditional Lexicographic Preference Trees by introducing multiple kernels in order to handle non-categorical attributes. Then, we define a learning strategy for inferring lexicographic rankers...
In some ordinal classification problems we know beforehand that the class label should be increasing (or decreasing) in the attributes. Such relations between class label and attributes are called monotone. We attempt to exploit such monotonicity constraints to reduce label noise. Noise may cause violations of the monotonicity constraint in the data set. In an attempt to reduce label noise, we make...
Melanoma is a type of cancer that usually occurs on the skin. Early detection is crucial for ensuring five-year survival (which varies between 15% and 99% depending on the melanoma stage). Melanoma severity is typically diagnosed by invasive methods (e.g. a biopsy). In this paper, we propose an alternative system combining image analysis and machine learning for detecting melanoma presence and severity...
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