27 papers · ranked by Valyu relevance
Syed Alberuni, Sumanta Ray
Extensive evidence recognizes that proteins associated with several diseases frequently interact with each other. This leads to develop different network-based methods for uncovering the molecular workings of human diseases. These methods are based on the idea that protein interaction networks act as maps, where…
Piotr Szymański, Tomasz Kajdanowicz
scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal operations. It provides native Python implementations of popular multi-label classification methods alongside a novel framework for label…
Hyukjun Gweon, Matthias Schonlau, Stefan H. Steiner, Diego Amancio
Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this article we propose a novel approach, Nearest Labelset using Double Distances…
M. Priyadharshini, A. Faritha Banu, Bhisham Sharma, Subrata Chowdhury + 3 more
'Subrata Chowdhury' 'Khaled Rabie' 'Thokozani Shongwe' 'Faheem Khan'] In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from…
Jesse Read, Fernando Pérez‐Cruz
—In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful…
Volker Roth, Bernd Fischer
Background We develop a probabilistic model for combining kernel matrices to predict the function of proteins. It extends previous approaches in that it can handle multiple labels which naturally appear in the context of protein function. Results Explicit modeling of multilabels significantly improves the capability of…
Bita Ghasemkhani, Ozlem Varliklar, Yunus Dogan, Semih Utku + 6 more
Simple Summary This study addresses the classification task in animal science, which helps organize and analyze complex data, essential for making informed decisions. It introduces Federated Multi-Label Learning (FMLL), a novel approach combining federated learning principles with a multi-label learning technique.…
Vikas Kumar, Arun K. Pujari, Vineet Padmanabhan, Venkateswara Rao Kagita
'Venkateswara Rao Kagita'] Multi-label learning is concerned with the classification of data with multiple class labels. This is in contrast to the traditional classification problem where every data instance has a single label. Due to the exponential size of output space, exploiting intrinsic information in feature…
Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl + 1 more
'Bernd Bischl'] Abstract We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr.…
Amirreza Mahdavi-Shahri, Mahboobeh Houshmand, Mahdi Yaghoobi, Mehrdad Jalali
'Mehrdad Jalali'] Abstract—in recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely…
Rajasekar Venkatesan, Meng Joo Er, Mihika Dave, Mahardhika Pratama + 1 more
'Shiqian Wu'] Abstract – In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class…
Alfonso E. Romero, Luis M. de Campos
Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an…
Azam Asilian Bidgoli, Hossein Ebrahimpour-Komleh, Shahryar Rahnamayan, Gang Mei
'Gang Mei'] Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many…
Rajasekar Venkatesan, Meng Joo Er, Shiqian Wu, Mahardhika Pratama
—In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed. Multi-label classification is one of the actively researched machine learning paradigm that has gained much attention in the recent years due to its rapidly increasing real world applications. In…
Hongbin Dong, Jing Sun, Xiaohang Sun, Adam Lipowski
Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing. However, high-dimensional information may contain noisy data, making the process of multi-label learning difficult. Feature…
Agamemnon Krasoulis, Kianoush Nazarpour
The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from…
Yanyi Chu, Xiaoqi Shan, Dennis R. Salahub, Yi Xiong + 1 more
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce heavily experiment cost, booming machine learning has been applied to this field and developed many computational methods, especially binary classification methods. However, there is still much room for…
Jinghou Ruan, Mingwei Wang, Deqing Liu, Maolin Chen + 2 more
'Friedhelm Schwenker'] In multi-label data, a sample is associated with multiple labels at the same time, and the computational complexity is manifested in the high-dimensional feature space as well as the interdependence and unbalanced distribution of labels, which leads to challenges regarding feature selection. As a…
Abrar Rahman Abir, Md Toki Tahmid, M. Saifur Rahman
Traditional methods for mRNA subcellular localization often fail to account for multiple compartmentalization. Recent multi-label models have improved performance, but still face challenges in capturing complex localization patterns. We introduce LOCAS (Localization with Supervised Contrastive Learning), which…
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…
Qi Zhang, Shan Li, Bin Yu, Yang Li + 3 more
Proteins play a significant part in life processes such as cell growth, development, and reproduction. Exploring protein subcellular localization (SCL) is a direct way to better understand the function of proteins in cells. Studies have found that more and more proteins belong to multiple subcellular locations, and…
Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie
Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
High throughput screening (HTS) is one of the leading techniques for hit identification in drug discovery and comprises of multiple phases, one primary and one or more confirmatory screens which result in multi-fidelity data. Noisy primary screening data are available on a large number of compounds and higher quality…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…
Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep + 3 more
Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active…