Classification in the multiple instance learning framework via spherical separation

Gaudioso, Manlio; Giallombardo, Giovanni; Miglionico, Giovanna; Vocaturo, Eugenio

doi:10.1007/s00500-019-04255-1

Cited by 25 publications

(12 citation statements)

References 18 publications

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“…The deployment of the multiple instances learning method may significantly improve prognosis prediction for cancer patients. Spherical separation surface-based approaches were observed to perform better than classifiers based on linear separation surfaces on the binary classification problems of two similar class labels, and may be utilized in future investigations [ 58 , 59 ]. Gender-specific prognosis may be investigated using biomedical imaging data and other data types in future studies.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

et al. 2021

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Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

et al. 2021

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“…Adopting additional sets of features together with more sophisticated algorithms providing nonlinear separation surfaces, such as in [44], opens up new possible scenarios for the implementation of efficient solutions, useful to provide a further support to specialists and, on the other hand, to have preliminary self diagnoses, since the diffusion of smartphones allows the possibility to take photos of one's own skin injuries. The use of medical solutions [45], as well as the possibility of creating Apps, BOTs and Educational Games [46], designed for different classes of population, are possible scenarios of interest.…”

Section: Discussionmentioning

confidence: 99%

Melanoma detection using color and texture features in computer vision systems

Fuduli¹,

Veltri²,

Vocaturo³

et al. 2019

Adv. sci. technol. eng. syst. j.

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All forms of skin cancer are becoming widespread. These forms of cancer, and melanoma in particular, are insidious and aggressive and if not treated promptly can be lethal to humans. Effective treatment of skin lesions depends strongly on the timeliness of the diagnosis: for this reason, artificial vision systems are required to play a crucial role in supporting the diagnosis of skin lesions. This work offers insights into the state of the art in the field of melanoma image classification. We include a numerical section where a preliminary analysis of some classification techniques is performed, using color and texture features on a data set constituted by plain photographies, to which no pre-processing technique has been applied. This is motivated by the necessity to open new horizons in creating self-diagnosis systems for accessible skin lesions, due also to a huge innovation of cameras, smartphones technology and wearable devices.

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“…Focusing in particular on the algorithmic side, the most relevant contribution has probably been provided by the methods based on the linearization of function f 2 (see, [41] and references therein), where the problem is tackled via successive convexifications of function f . In the last years, nonsmooth-tailored DC programming has experienced a lot of attention as it has a lot of practical applications (see [28,42]). In fact, several nonsmooth DC algorithms have been developed ( [30,[43][44][45][46][47]).…”

Section: Solving Dc-mil Using a Nonsmooth DC Algorithmmentioning

confidence: 99%

“…We compare our DC-MIL approach against the algorithms mi-SVM [18], MI-SVM [18], MICA [22], MIL-RL [19], and for medium-size problems also against the MIC Bundle [21] and DC-SMIL [28]. All such methods have been briefly surveyed in the introduction section.…”

Section: Data Set Dimension Instances Bagsmentioning

confidence: 99%

“…Recently, nonlinear instance-space MIL classifiers have also been proposed in the literature, such as in [27] and in [28], where a spherical separation approach is adopted: in particular, in the former a variable neighborhood search method [29] is used, while in the latter a DC (difference of convex) model is solved using an appropriate DC algorithm [30]. In passing, we stress that many DC models have been introduced in machine learning, in the supervised [31][32][33][34][35], semisupervised [36,37] and unsupervised cases [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

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SVM-Based Multiple Instance Classification via DC Optimization

et al. 2019

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A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the points inside the bags are unknown. We focus on the binary classification case, where the objective is to discriminate between positive and negative bags using a separating surface. Adopting a support vector machine setting at the training level, the problem of minimizing the classification-error function can be formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex (DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC algorithm. Some of the numerical results on benchmark data sets are reported.

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Classification in the multiple instance learning framework via spherical separation

Cited by 25 publications

References 18 publications

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Melanoma detection using color and texture features in computer vision systems

SVM-Based Multiple Instance Classification via DC Optimization

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