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This paper provides a novel and unified framework of representation based classification technique. The proposed atomic representation based classification (ARC) framework includes, but not limited to, sparse representation based classification (SRC), low-rank representation based classification (LRRC) as special cases. Despite good performance, most existing classification methods are heavily reliant...
While the generalizability of classifiers receive much attention in research, interpretability is often neglected. This paper proposes a rule-plus-exemplar classification framework based on ideas in cognitive psychology. The classification process is interpretable and intuitive, and also generalizes well. It can perform better than other interpretable methods such as decision trees, for both interpolative...
Currently, network intrusion detection is in face of the conflict between the difficult to label data and the high accuracy request to detect intrusion. In this paper, we propose a SVM co-training based method to detect network intrusion. It exploits the large amount of unlabeled data, and increase the detection accuracy and stability by co-training two classifiers. The simulation results show that...
In real world classification tasks, the original instances are represented by raw features. Usually domain related algorithms are needed to extract discriminative features. But the algorithms selection and additional parameters tuning are difficult for people with little domain knowledge and experience. In this paper, a new machine learning framework called "decompose learning" is proposed...
We investigate classification algorithms LDA, SPRT and a modified SPRT on clinical datasets for Parkinson's disease, colon cancer, and breast cancer. The SPRT algorithms were run with components in decreasing variance order and random order. Results for those in random order were calculated as the majority predictions over 100 runs. Truncation was always set to the total number of components of the...
Supervised classification has been extensively addressed in the literature as it has many applications, especially for text categorization or web content mining where data are organized through a hierarchy. On the other hand, the automatic analysis of brand names can be viewed as a special case of text management, although such names are very different from classical data. They are indeed often neologisms,...
Evaluation of pattern classification systems is the critical and important step in order to understand the system's performance over a chosen testing dataset. In general, considering cross validation can produce the `optimal' or `objective' classification result. As some ground-truth dataset(s) are usually used for simulating the system's classification performance, this may be somehow difficult to...
It is known that Logistic Regression coupled with Partial Least Squares dimension reduction (PLSDR-LD) is capable of extracting a great deal of useful information for classification from gene expression profile and getting a rather high classification accuracy rate. In this study, we replace the logistic function of Logistic Regression with several functions which are similar to logistic function...
The continued growth of Email usage, which is naturally followed by an increase in unsolicited emails so called spams, motivates research in spam filtering area. In the context of spam filtering systems, addressing the evolving nature of spams, which leads to obsolete the related models, has been always a challenge. In this paper an adaptive spam filtering system based on language model is proposed...
In this work we have reformulated the twin support vector machine (TWSVM) classifier by considering unity norm of the normal vector of the hyperplanes as the constraints. TWSVM with unity norm hyperplanes removes the shortcomings of the classical TWSVM formulation. The resulting new formulation is a nonlinear programming problem which is solved by sequential quadratic optimization method. The performance...
Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute B, find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute B. This problem is motivated by the fact that often available historic data is biased due to discrimination, e.g., when B denotes ethnicity. Using...
Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
In this paper, a supervised feature selection approach is presented, which is based on support vector data description(SVDD). This method is suggested for multi-class classification case, and it utilizes a sequential backward selection algorithm using the accuracy of classifier to decide which feature to be eliminated. The proposed approach is applied to well-known real world datasets, and the obtained...
Random forest is an excellent ensemble learning method, which is composed of multiple decision trees grown on random input samples and splitting nodes on a random subset of features. Due to its good classification and generalization ability, random forest has achieved success in various domains. However, random forest will generate many noisy trees when it learns from the data set that has high dimension...
In some machine learning applications using soft labels is more useful and informative than crisp labels. Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. In this paper we propose an approach for Fuzzy-Input Fuzzy-Output...
In this paper, we propose a tensorial approach to single trial recognition in a EEG-based BCI system related to movement related potentials. In this approach input data are considered as tensors instead of more conventional vector or matrix representations. Feature extraction for multiway EEG spectral tensors is solved by using tensor (multi-array) decompositions. For the same EEG motor imagery dataset,...
Smart phones have become a powerful platform for wearable context recognition. We present a service-based recognition architecture which creates an evolving classification system using feedback from the user community. The approach utilizes classifiers based on fuzzy inference systems which use live annotation to personalize the classifier instance on the device. Our recognition system is designed...
The performance of a classification model depends not only on the algorithm by which the model is learned, but also on the training set. Manual annotation of the training data is a tedious and time consuming job. In order to overcome the problem of laborious hand-labeling of a large training set, a set of techniques called semi-supervised learning was designed. Co-training is one of the major semi-supervised...
In this paper, we propose a method of obtaining the sense of touch by using the EEG. Multimodal device based on human sensory systems have obtained a lot of attention in the fields of human interface. Among the human sensory systems, especially, the sense of touch is important form of social interaction and it can have powerful emotional consequences. Therefore, it is important to improve tactile...
Active learning methods seek to reduce the number of labeled instances needed to train an effective classifier. Most current methods assume the availability of some reasonable amount of initially labeled training data so that the learners can be trained with sufficient quality. However, for many applications, the amount of initial training data is often limited, this will affect the quality of the...
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