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About 10,000 papers for “binary classification”

Perceptron algorithm for binary classification

Juan Císcar, Alfonso, Civera Saiz, Jorge, Sanchis Navarro, José Alberto · Repositorio Institucional de la Universitat Politècnica (Universitat Politècnica de València) · 2019

The training objectives of the learning object are: 1) To interpret linear classifiers in the binary case; 2) To apply the Perceptron algorithm in the binary case; 3) To know Novikoff¿s convergence theorem; and 4) To know the basic limitations of the Perceptron algorithm.

Cited by 0

NLS KDD binary classification results.

Mohammed Tawfik (19287598) · Figshare · 2024

<div><p>The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constraints such as limited computing resources at fog nodes render conventional intrusion detection techniques impractical. This paper proposes a novel framework that integrates stacked autoencoders, CatBoost, and an optimised transformer-CNN-LSTM ensemble tailored for intrusion detection in fog and IoT networks. Autoencoders extract robust features from high-dimensional traffic data while reducing the dimensionality of the efficiency at fog nodes. CatBoost refines features through predictive selection. The ensemble model combines self-attention, convolutions, and recurrence for comprehensive traffic analysis in the cloud. Evaluations of the NSL-KDD, UNSW-NB15, and AWID benchmarks demonstrate an accuracy of over 99% in detecting threats across traditional, hybrid enterprises and wireless environments. Integrated edge preprocessing and cloud-based ensemble learning pipelines enable efficient and accurate anomaly detection. The results highlight the viability of securing real-world fog and the IoT infrastructure against continuously evolving cyber-attacks.</p></div>

Cited by 0Open access

Classification with binary gene expressions

Salih Tuna, Mahesan Niranjan · Journal of Biomedical Science and Engineering · 2009

Microarray gene expression measurements are reported, used and archived usually to high numerical precision. However, properties of mRNA molecules, such as their low stability and availability in small copy numbers, and the fact that measurements correspond to a population of cells, rather than a single cell, makes high precision meaningless. Recent work shows that reducing measurement precision leads to very little loss of information, right down to binary levels. In this paper we show how properties of binary spaces can be useful in making inferences from microarray data. In particular, we use the Tanimoto similarity metric for binary vectors, which has been used effectively in the Chemoinformatics literature for retrieving chemical compounds with certain functional properties. This measure, when incorporated in a kernel framework, helps recover any information lost by quantization. By implementing a spectral clustering framework, we further show that a second reason for high performance from the Tanimoto metric can be traced back to a hitherto unnoticed systematic variability in array data: Probe level uncertainties are systematically lower for arrays with large numbers of expressed genes. While we offer no molecular level explanation for this systematic variability, that it could be exploited in a suitable similarity metric is a useful observation in itself. We further show preliminary results that working with binary data considerably reduces variability in the results across choice of algorithms in the preprocessing stages of microarray analysis.

Cited by 13Open access

Classification of binary acentric tellurides

Victor V. Atuchin∥⊥, B. I. Kidyarov · 2010

Structural parameters and physical properties are considered for >100 presently known simple and binary acentric telluride crystals. Distribution of the crystals over point symmetry groups has been defined in comparison with that of selenides. A rosette of ellipses of acentricity has been outlined on the plane of the shortest chemical bond lengths metal-tellurium in the crystal lattice. A set of elements most promising for pronounced nonlinear optical properties has been reported.

Cited by 0

Shellcode classification analysis with binary classification-based machine learning

Jaka Naufal Semendawai, Deris Stiawan, Iwan Pahendra Anto Saputra et al. · International Journal of Informatics and Communication Technology (IJ-ICT) · 2025

The internet enables people to connect through their devices. While it offers numerous benefits, it also has adverse effects. A prime example is malware, which can damage or even destroy a device or harm its users, highlighting the importance of cyber security. Various methods can be employed to prevent or detect malware, including machine learning techniques. The experiments are based on training and testing data from the UNSW_NB15 dataset. K-nearest neighbor (KNN), decision tree, and Naïve Bayes classifiers determine whether a record in the test data represents a Shellcode attack or a non-Shellcode attack. The KNN, decision tree, and Naïve Bayes classifiers reached accuracy rates of 96.26%, 97.19%, and 57.57%, respectively. This study's findings aim to offer valuable insights into the application of machine learning to detect or classify malware and other forms of cyberattacks.

Cited by 0Open access

Multi-class least squares classification at binary-classification complexity

Zineb Noumir, Paul Honeiné, Cédric Richard · HAL (Le Centre pour la Communication Scientifique Directe) · 2011

This paper deals with multi-class classification problems. Many methods extend binary classifiers to operate a multi-class task, with strategies such as the one-vs-one and the one-vs-all schemes. However, the computational cost of such techniques is highly dependent on the number of available classes. We present a method for multi-class classification, with a computational complexity essentially independent of the number of classes. To this end, we exploit recent developments in multifunctional optimization in machine learning. We show that in the proposed algorithm, labels only appear in terms of inner products, in the same way as input data emerge as inner products in kernel machines via the so-called the kernel trick. Experimental results on real data show that the proposed method reduces efficiently the computational time of the classification task without sacrificing its generalization ability.

Cited by 2Open access

Shellcode Classification with Machine Learning Based on Binary Classification

Jaka Naufal Semendawai, Deris Stiawan, Iwan Pahendra · Jurnal Indonesia Sosial Teknologi · 2025

The Internet can link one person to another using their respective devices. The internet itself has both positive and negative impacts. One example of the internet's negative impact is malware that can disrupt or even kill a device or its users; that is why cyber security is required. Many methods can be used to prevent or detect malware. One of the efforts is to use machine learning techniques. The training and testing dataset for the experiments is derived from the UNSW_NB15 dataset. K-Nearest Neighbour (KNN), Decision Tree, and Naïve Bayes classifiers are implemented to classify whether a record in the testing data is Shellcode or non-Shellcode attack. The KNN, Decision Tree, and Naïve Bayes classifiers achieve accuracy levels of 96.82%, 97.08%, and 63.43%, respectively. The results of this research are expected to provide insight into the use of machine learning in detecting or classifying malware or other types of cyber attacks.

Cited by 1Open access

Robust Binary Audio Classification with SNNs

Krishaan Chaudhary, Eashan Patel · Zenodo (CERN European Organization for Nuclear Research) · 2026

Spiking neural networks (SNNs) are an alternative to conventional deep learning methods like artificial neural networks (ANNs) for temporal sensory data, with the potential for more brain-like and energy-efficient processing. We study a specific setting: binary classification of one-second spoken words from the Google Speech Commands dataset. Audio is converted into event-based spike trains using the Speech2Spikes encoding pipeline. We train a fully-connected SNN with leaky integrate-andfire (LIF) neurons using surrogate gradients and a rate-based MSE objective function. To evaluate robustness, we introduce two distortions, white noise and random pitch shifting, and compare (i) training only on clean data versus (ii) training on a mixture of clean and distorted data. We also compare the efficacy of these SNN classifiers with baseline ANN classifiers under similar parameters for model architecture and data representation.

Cited by 0Open access

Binary convolution model for image classification

Solis E., Shashev D., Solis E., Shashev D. · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2024

Binary Neural Networks present an opportunity for developing Neural Networks that require less computing power as well as energy. This is done through the use of binary values for weights and inputs. This research presents an architecture for image classification where the image and the Convolutional Layers are binarized. The result of the binary convolution is then fed to a non-binary network.

Cited by 0

Binary Classification and the Subtractive Approach.

Gregory M. Corso, Simon P Kelly, D. E. Bridges · 1983

Abstract : Two experiments were performed using a modification of the subtractive approach to the partitioning of human reaction time. This investigation provided information concerning the effects of display size, classification rules, and interference stimuli on input and output latencies. The major findings from this investigation showed that input and output latencies were affected by different independent variables and provided information regarding the cognitive processes within a binary classification task not observed with previous methods. Suggestions for further research are presented.

Cited by 0

Binary Classification Based Monte Carlo Simulation

Elouan Argouarc'h, François Desbouvries · IEEE Signal Processing Letters · 2024

Acceptance-rejection (AR), Independent Metropolis Hastings (IMH) or Importance Sampling (IS) Monte Carlo (MC) algorithms all involve computing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ratios</i> of two probability density functions (pdf) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p_{1}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p_{0}$</tex-math></inline-formula>. On the other hand, classifiers discriminate samples produced by a binary mixture and can be used to approximate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ratio</i> of corresponding pdfs. We therefore establish a bridge between simulation and classification, which enables us to propose pdf-free versions of ratio-based simulation algorithms, where the ratio is replaced by a surrogate function computed via a classifier. Our modified samplers are based on very different hypotheses: the knowledge of functions <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p_{1}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p_{0}$</tex-math></inline-formula> is relaxed (- they may be totally unknown), and is counterbalanced by the availability of a classification function, which can be obtained from a labelled dataset. From a probabilistic modeling perspective, our procedure involves a structured energy based model which can easily be trained and is structurally compatible with the classical samplers.

Cited by 0Open access

Support vector machines for binary classification

A C C Coolen, Reimer Kühn, Peter Sollich · 2005

Abstract Support vector machines (SVMs) carry out binary classifications. They involve a preprocessing stage which aims to transform data sets that are not linearly separable into ones that are. First, however, we will have to return to the properties of linearly separable problems, for reasons which will become clear. The preprocessing will be introduced afterwards.

Cited by 13

Binary classification models with "Uncertain" predictions

Damjan Krstajić, Ljubomir Buturović, Simon Thomas et al. · arXiv (Cornell University) · 2017

Binary classification models which can assign probabilities to categories such as "the tissue is 75% likely to be tumorous" or "the chemical is 25% likely to be toxic" are well understood statistically, but their utility as an input to decision making is less well explored. We argue that users need to know which is the most probable outcome, how likely that is to be true and, in addition, whether the model is capable enough to provide an answer. It is the last case, where the potential outcomes of the model explicitly include "don't know" that is addressed in this paper. Including this outcome would better separate those predictions that can lead directly to a decision from those where more data is needed. Where models produce an "Uncertain" answer similar to a human reply of "don't know" or "50:50" in the examples we refer to earlier, this would translate to actions such as "operate on tumour" or "remove compound from use" where the models give a "more true than not" answer. Where the models judge the result "Uncertain" the practical decision might be "carry out more detailed laboratory testing of compound" or "commission new tissue analyses". The paper presents several examples where we first analyse the effect of its introduction, then present a methodology for separating "Uncertain" from binary predictions and finally, we provide arguments for its use in practice.

Cited by 4Open access

Predicting Vectorization Profitability Using Binary Classification

Antoine Trouvé, Arnaldo J. CRUZ, Dhouha BEN BRAHIM et al. · IEICE Transactions on Information and Systems · 2014

Basic block vectorization consists in realizing instruction-level parallelism inside basic blocks in order to generate SIMD instructions and thus speedup data processing. It is however problematic, because the vectorized program may actually be slower than the original one. Therefore, it would be useful to predict beforehand whether or not vectorization will actually produce any speedup. This paper proposes to do so by expressing vectorization profitability as a classification problem, and by predicting it using a machine learning technique called support vector machine (SVM). It considers three compilers (icc, gcc and llvm), and a benchmark suite made of 151 loops, unrolled with factors ranging from 1 to 20. The paper further proposes a technique that combines the results of two SVMs to reach 99% of accuracy for all three compilers. Moreover, by correctly predicting unprofitable vectorizations, the technique presented in this paper provides speedups of up to 2.16 times, 2.47 times and 3.83 times for icc, gcc and LLVM, respectively (9%, 18% and 56% on average). It also lowers to less than 1% the probability of the compiler generating a slower program with vectorization turned on (from more than 25% for the compilers alone).

Cited by 5Open access

Optical Neural Classification Of Binary Patterns

Steven C. Gustafson, Gordon R. Little · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1988

Binary pattern classification that may be implemented using optical hardware and neural network algorithms is considered. Pattern classification problems that have no concise description (as in classifying handwritten characters) or no concise computation (as in NP-complete problems) are expected to be particularly amenable to this approach. For example, optical processors that efficiently classify binary patterns in accordance with their Boolean function complexity might be designed. As a candidate for such a design, an optical neural network model is discussed that is designed for binary pattern classification and that consists of an optical resonator with a dynamic multiplex-recorded reflection hologram and a phase conjugate mirror with thresholding and gain. In this model, learning or training examples of binary patterns may be recorded on the hologram such that one bit in each pattern marks the pattern class. Any input pattern, including one with an unknown class or marker bit, will be modified by a large number of parallel interactions with the reflection hologram and nonlinear mirror. After perhaps several seconds and 100 billion interactions, a steady-state pattern may develop with a marker bit that represents a minimum-Boolean-complexity classification of the input pattern. Computer simulations are presented that illustrate progress in understanding the behavior of this model and in developing a processor design that could have commanding and enduring performance advantages compared to current pattern classification techniques.

Cited by 1