22 papers · ranked by Valyu relevance
Shehroz S. Khan, Michael G. Madden
One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been…
Poojan Oza, Vishal M. Patel
—We present a novel Convolutional Neural Network (CNN) based approach for one class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the crossentropy loss to learn a good representation as well as the decision boundary for the…
Paweł Cichosz, Stanisław Kozdrowski, Sławomir Sujecki, Jan Kozak + 1 more
'Przemysław Juszczuk'] Applying machine learning algorithms for assessing the transmission quality in optical networks is associated with substantial challenges. Datasets that could provide training instances tend to be small and heavily imbalanced. This requires applying imbalanced compensation techniques when using…
Amir Ahmad
One-class classification (OCC) deals with the classification problem in which the training data have data points belonging only to the target class. In this paper, we present a one-class classification algorithm, One-Class Classification by Ensembles of Random Plane (OCCERP), that uses random planes to address OCC…
Itziar Irigoien, Basilio Sierra, Concepción Arenas
In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In…
Henrique O. Marques, Lorne Swersky, Jörg Sander, Ricardo J. G. B. Campello + 1 more
'Ricardo J. G. B. Campello' 'Arthur Zimek'] It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem (Janssens and Postma, in: Proceedings of the 18th annual Belgian-Dutch on machine learning, pp 56-64, 2009; Janssens et al. in: Proceedings of the 2009 ICMLA…
Chi Zhang, Dmytro Antypov, Matthew J Rosseinsky, Matthew Stephen Dyer
Machine learning has found wide application in the materials field, particularly in discovering structure-property relationships. However, its potential in predicting synthetic accessibility of materials remains relatively unexplored due to the lack of negative data. In this study, we employ several one-class…
Pratik K. Mishra, Chandan Gautam, Aruna Tiwari
One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was developed for the OCC task. This paper introduces a novel extension of this classifier by embedding minimum variance information within its architecture and is…
Do Gyun Kim, Jin Young Choi
One of the most rising issues in recent machine learning research is One-Class Classification which considers data set composed of only one class and outliers. It is more reasonable than traditional Multi-Class Classification in dealing with some problematic data set or special cases. Generally, classification accuracy…
Oliver Urs Lenz, Daniel Peralta, Chris Cornelis
One-class classification is a challenging subfield of machine learning in which so-called data descriptors are used to predict membership of a class based solely on positive examples of that class, and no counter-examples. A number of data descriptors that have been shown to perform well in previous studies of…
Chandan Gautam, Aruna Tiwari, Qian Leng
One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods and their thirteen…
James Harnly
One-class modeling is a supervised multivariate botanical identification method based on principal component analysis (PCA) that constructs a model based only on the characteristics of the reference samples and uses the Q statistic as a combined metric. Test samples are judged to be similar (authentic) if their…
Chandan Gautam, Aruna Tiwari, Suresh Sundaram, Alexandros Iosifidis
In this paper, a multi-layer architecture (in a hierarchical fashion) by stacking various Kernel Ridge Regression (KRR) based Auto-Encoder for one-class classification is proposed and is referred as MKOC. MKOC has many layers of Auto-Encoders to project the input features into new feature space and the last layer is…
Xuan Wang, Jiro Tanaka
Biometric authentication is popular in authentication systems, and gesture as a carrier of behavior characteristics has the advantages of being difficult to imitate and containing abundant information. This research aims to use three-dimensional (3D) depth information of gesture movement to perform authentication with…
Joseph Silburt, Isabelle Aubert
In cases of brain injury, degeneration and repair, defining microglia and astrocytic activation using cellular markers alone remains a challenging task. We developed MORPHIOUS, an unsupervised machine learning workflow that utilizes a one-class support vector machine to segment clusters of activated glia by only…
Stamatia Zavitsanou, Zonghua Bo, Emanuele Casali, Matthew Langton + 1 more
Machine learning (ML) is currently transforming the field of chemistry by offering unparalleled efficiency in addressing complex challenges. Despite the progress made, a notable gap persists in the availability of user-friendly tools tailored to chemical problems involving small and sparse datasets. Here, we introduce…
Henning Tiedemann, Yaniv Morgenstern, Filipp Schmidt, Roland W Fleming
Humans have the striking ability to learn and generalize new visual concepts from just a single exemplar. We suggest that when presented with a novel object, observers identify its significant features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants. To test this, we…
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
Jesse Sherwood, Jesse Lowe, Reza Derakhshani
[Finding suitable common feature sets for use in multiclass subject independent brain-computer interface (BCI) classifiers is problematic due to characteristically large inter-subject variation of electroencephalographic signatures. We propose a wrapper search method using a one versus the rest discrete output…
Authors not listed
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with…
Mojtaba Nayyeri, Hossein Sharifi Noghabi
Cancer prediction is of great importance and significance and it is crucial to provide researchers and scientists with novel, accurate and robust computational tools for this issue. Recent technologies such as Microarray and Next Generation Sequencing have paved the way for computational methods and techniques to play…