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Partial label learning (PLL) handles data classification problems by assigning a candidate label set to each sample. There is always one correct label in a candidate label set. Since the PLL can achieve classification without precise labels, it ...

Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in ...

• Background: Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies.
• Challenge: One of the challenges encountered in integrating omics data is the ...

Multi-label classification aims to construct prediction models from input space to output space for multi-label datesets. However, the feature space of multi-label dataset and the hypothesis space of classifier are always complex. In order to ...

The real estate industry relies heavily on accurately predicting the price of a house based on numerous factors such as size, location, amenities, and season. In this study, we explore the use of machine learning techniques for predicting house ...

With the widespread application of high-throughput technologies, multi-omics data are playing an increasingly important role in cancer subtyping. However, the heterogeneity and high-dimensionality of different omics data make it a challenging task ...

Searching for quantitative scientific prediction models and methods under uncertain hybrid information environment is helpful to the scientific decision-making of practical management problems. In this paper, we discuss the uncertain decision ...

The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, ...

• The severity prediction of pulmonary diseases was considered as a time estimation problem, and label distribution was introduced to describe the conversion time from non-severe stage to severe stage.
• CS-LD-MKSVR is proposed to deal ...

• Our algorithm works on incomplete multi-view data with arbitrary missing patterns.

A fundamental assumption underpinning the recent progress in multi-view clustering is the full observation of all views, which rarely holds for real-world data as they often suffer from the absence of some instances in individual ...

The price of a house depends on many factors, such as its size, location, amenities, surrounding establishments, and the season in which the house is being sold, just to name a few of them. As a seller, it is absolutely essential to price the ...

Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min–max optimization (namely MKGF-...

• The min–max weight strategy improves the quality of the combined kernel and the fusion graph.

Hyperspectral unmixing (HU) is regarded as an indispensable preprocessing procedure for many field of spectral data analysis because of the existence of mixed pixels. However, the unmixing algorithms are implemented under the ...

Existing methods for egocentric activity recognition are mostly based on extracting motion characteristics from videos. On the other hand, ubiquity of wearable sensors allow acquisition of information from different sources. Although the increase ...

Given the increasing amounts of data and high feature dimensionalities in forecasting problems, it is challenging to build regression models that are both computationally efficient and highly accurate. Moreover, regression models commonly suffer ...

Despite excellent performance shown by spatially regularized discriminative correlation filters (SRDCF) for visual tracking, some issues remain open that hinder further boosting their performance: first, SRDCF utilizes multiple training images to ...

Advances in digital electronics, wireless communications, and electro-mechanical systems technology have revolutionized the society and economy across the globe by enabling the development of low-cost, low-power, and multi-functional sensor nodes, ...

• Multitask learning framework for virulent protein prediction to handle data scarcity.

The ability of multitask learning promulgated its sovereignty in the machine learning field with the diversified application including but not limited to bioinformatics and pattern recognition. Bioinformatics provides a wide range of ...

In this paper, co-regularized kernel ensemble regression scheme is brought forward. In the scheme, multiple kernel regressors are absorbed into a unified ensemble regression framework simultaneously and co-regularized by minimizing total loss of ...

The problem discussed in this paper stems from a project of cellular network traffic prediction, the primary step of network planning striving to serve the continuously soaring network traffic best with limited resource. The traffic prediction ...

Multi-kernel extreme learning machine based method is proposed for EEG classification.Supplementary information from different kernels are integrated for better accuracy.Extensive experimental comparison confirms superiority of the proposed method. One ...

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming ...