arXiv research

Paper explores reducing precision in SVM for faster text classification.

Proposes a new tree-based algorithm for class-imbalanced data.

Paper tackles imbalanced binary classification by optimizing precision and recall directly.

Paper analyzes multiclass classification with high-dimensional data.

Study compares ML and DL methods for autism classification.

For information retrieval and binary classification, we show that precision at the top (or precision at k) and recall at the top (or recall at k) are maximised by thresholding the posterior probability of the positive class. This finding is a consequence of a result on constrained minimisation of the cost-sensitive exp…

Paper proposes ARB-Loss to improve classification precision in imbalanced datasets.

It is well-known that the precision of data, hyperparameters, and internal representations employed in learning systems directly impacts its energy, throughput, and latency. The precision requirements for the training algorithm are also important for systems that learn on-the-fly. Prior work has shown that the data and…

We propose a penalized likelihood method to fit the linear discriminant analysis model when the predictor is matrix valued. We simultaneously estimate the means and the precision matrix, which we assume has a Kronecker product decomposition. Our penalties encourage pairs of response category mean matrices to have equal…

This article provides, through theoretical analysis, an in-depth understanding of the classification performance of the empirical risk minimization framework, in both ridge-regularized and unregularized cases, when high dimensional data are considered. Focusing on the fundamental problem of separating a two-class Gauss…

Rule-based classifiers quantify uncertainty using Bernoulli random variables.

Proposes PQ, a more precise Bayesian quantifier for prevalence estimation.

In this article we propose a novel ranking algorithm, referred to as HierLPR, for the multi-label classification problem when the candidate labels follow a known hierarchical structure. HierLPR is motivated by a new metric called eAUC that we design to assess the ranking of classification decisions. This metric, associ…

We give a diffeomorphism classification of pinched negatively curved manifolds with amenable fundamental groups, namely, they are precisely the Möbius band, and the products of a line with the total spaces of flat vector bundles over closed infranilmanifolds.

Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a cruc…

Enhanced anomaly detection using PRC-RF with autoencoders.

A number of classification problems need to deal with data imbalance between classes. Often it is desired to have a high recall on the minority class while maintaining a high precision on the majority class. In this paper, we review a number of resampling techniques proposed in literature to handle unbalanced datasets …

We consider the post-training quantization problem, which discretizes the weights of pre-trained deep neural networks without re-training the model. We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit number…

The paper analyzes adversarial training effects on classification accuracy.

A new metric evaluates classification algorithms at the point of indistinguishability.

HMQ improves quantization for edge devices with mixed precision.

This paper compares average-K and top-K classification methods under ambiguity.

We give complete classification of C^2-regular and non-degenerate projectively Anosov flows on three dimensional manifolds. More precisely, we prove that such a flow on a connected manifold must be either an Anosov flow or represented as a finite union of $T^2 \times [0,1]$-models.

RCAM-based ensemble combines binary classifiers using similarity and vote scheme.

System detects financial opportunities in tweets with high precision.

The paper predicts diseases using both clinical and genomics data.

In recent era prediction of enzyme class from an unknown protein is one of the challenging tasks in bioinformatics. Day to day the number of proteins is increases as result the prediction of enzyme class gives a new opportunity to bioinformatics scholars. The prime objective of this article is to implement the machine …

This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data. It uses a Deep Convolutional Autoencoder (DCAE) for learning compact feature spaces, in combination with recently-proposed Reinforcement Learni…

We investigate the classification of topological quandles on some simple manifolds. Precisely we classify all Alexander quandle structures, up to isomorphism, on the real line and the unit circle. For the closed unit interval $[0, 1]$, we conjecture that there exists only one topological quandle structure on it, i.e. t…

Paper introduces hierarchical softmax for global hierarchical classification tasks.

This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an arra…

This paper tackles unbiased loss functions for multilabel classification with missing labels.

Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.

In recent years, dynamically growing data and incrementally growing number of classes pose new challenges to large-scale data classification research. Most traditional methods struggle to balance the precision and computational burden when data and its number of classes increased. However, some methods are with weak pr…

Algorithm improves SVM classification in non-Euclidean spaces.

SPEQ improves quantized neural networks by stochastic precision sharing and cosine similarity loss.

We show there are precisely 15 inhomogeneous biquotients of the form $Sp(3)// Sp(1)^2$ and show that at least 8 of them admit metrics of quasi-positive curvature.

Study of 4D Ricci solitons with symmetry, finding precise geometric asymptotics.

There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently been introduced as a method for transductive (semi-supervised) multi-label classification, and has seve…

Study finds numerical moduli in special 2-flags of length 5.

In this note, we complete the classification of quasi-alternating Montesinos links. We show that the quasi-alternating Montesinos links are precisely those identified independently by Qazaqzeh-Chbili-Qublan and Champanerkar-Ording. A consequence of our proof is that a Montesinos link $L$ is quasi-alternating if and onl…

The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. Although extreme quantization has proven to be a powerful tool to achieve significan…

New approach improves classification guarantees by focusing on direction rather than regression risk.

Analysis of somatic mutation profiles from cancer patients is essential in the development of cancer research. However, the low frequency of most mutations and the varying rates of mutations across patients makes the data extremely challenging to statistically analyze as well as difficult to use in classification probl…

Convolution and pooling improve kernel methods in image classification.

In most machine learning applications, classification accuracy is not the primary metric of interest. Binary classifiers which face class imbalance are often evaluated by the $F_β$ score, area under the precision-recall curve, Precision at K, and more. The maximization of many of these metrics can be expressed as a con…

Conformal prediction ensures reliable weed spraying guarantees.

Improved classification model for high-cardinality categorical predictors.