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Naive Bayes (NB) is one of the top ten machine learning algorithms whereas its attribute independence assumption rarely holds in practice. A feasible and efficient approach to improving NB is relaxing the assumption by adding augmented edges to the restricted topology of NB. In this paper we prove theoretically that the generalized topology may be a suboptimal solution to model multivariate probability distributions if its fitness to data cannot be measured. Thus we propose to apply log-likelihood function as the scoring function, then introduce an efficient heuristic search strategy to explore high-dependence relationships, and for each iteration the learned topology will be improved to fit data better. The proposed algorithm, called log-likelihood Bayesian classifier (LLBC), can respectively learn two submodels from labeled training set and individual unlabeled testing instance, and then make them work jointly for classification in the framework of ensemble learning. Our extensive experimental evaluations on 36 benchmark datasets from the University of California at Irvine (UCI) machine learning repository reveal that, LLBC demonstrates excellent classification performance and provides a competitive approach to learn from labeled and unlabeled data. [ABSTRACT FROM AUTHOR]

Purpose: The tracheoesophageal puncture for the voice prosthesis (VP) placement is the recognized gold standard in post-laryngectomy voice rehabilitation. Despite the development of specific intraoperative techniques, a subset of patients will suffer from poor functional outcomes due to pharyngoesophageal spasms (PES). This paper evaluates the functional outcomes after transcutaneous botulinum toxin type A (BTX-A) infiltration for PES with a videofluoroscopy-guided technique. Methods: Since 2022, eight consecutive patients with VP and affected by PES were treated with BTX-A injection by a standard videofluoroscopic guided technique at the European Institute of Oncology, IRCCS (IEO) in Milan. A lidocaine test was performed pre-operatively to evaluate the potential effect of chemical neurectomy. All patients with positive lidocaine tests were injected with 50 IU of BTX-A (Allergan, Irvine, CA) according to the sites marked during the videofluoroscopy. Reported symptoms (VHI, SECEL), perceptual (INFVo), aerodynamic (MPT) and manometric parameters were collected before and after treatment. Results: In all cases, BTX-A was performed as an outpatient procedure without complications. For seven patients, only one BTX-A injection was needed, while one patient required a re-injection. Subjective and perceptive improvement after BTX-A was significant for VHI, SECEL and INFVo. MPT showed significant improvement after a chemical neurectomy. After a mean follow-up of 6 months, all patients maintained a good TES quality. Conclusion: The videofluoroscopic guided BTX-A injection of the pharyngoesophageal tract showed to be a feasible and reproducible technique in all cases. The pharyngoesophageal videofluoroscopy allows defining of patients' anatomical landmarks that help the surgeon to perform a homogeneous injection, empowered by post-injection massage. [ABSTRACT FROM AUTHOR]

Averaged one-dependence estimators (AODE) is a state-of-the-art machine learning tool for classification due to its simplicity, high computational efficiency, and excellent classification accuracy. Weighting provides an effective mechanism to ensemble superparent one-dependence estimators (SPODEs) in AODE by linearly aggregating their weighted probability estimates. Supervised weighting and unsupervised weighting are proposed to learn weights from labeled or unlabeled data, whereas their interoperability has not previously been investigated. In this paper, we propose a novel weighting paradigm in the framework of semi-supervised learning, called semi-supervised weighting (SSW). Two different versions of weighted AODEs, supervised weighted AODE (SWAODE) which performs weighting at training time and unsupervised weighted AODE (UWAODE) which performs weighting at classification time, are built severally. Log likelihood function is introduced to linearly aggregate the outcomes of these two weighted AODEs. The proposed algorithm, called SSWAODE, is validated on 38 benchmark datasets from the University of California at Irvine (UCI) machine learning repository and the experimental results prove the effectiveness and robustness of SSW for weighting AODE in terms of zero-one loss, bias, variance and etc. SSWAODE well achieves the balance between the ground-truth dependencies approximation and the effectiveness of probability estimation. [ABSTRACT FROM AUTHOR]

In the design of classification models, irrelevant or noisy features are often generated. In some cases, there may even be negative interactions among features. These weaknesses can degrade the performance of the models. Feature selection is a task that searches for a small subset of relevant features from the original set that generate the most efficient models possible. In addition to improving the efficiency of the models, feature selection confers other advantages, such as greater ease in the generation of the necessary data as well as clearer and more interpretable models. In the case of medical applications, feature selection may help to distinguish which characteristics, habits, and factors have the greatest impact on the onset of diseases. However, feature selection is a complex task due to the large number of possible solutions. In the last few years, methods based on different metaheuristic strategies, mainly evolutionary algorithms, have been proposed. The motivation of this work is to develop a method that outperforms previous methods, with the benefits that this implies especially in the medical field. More precisely, the present study proposes a simple method based on tabu search and multistart techniques. The proposed method was analyzed and compared to other methods by testing their performance on several medical databases. Specifically, eight databases belong to the well-known repository of the University of California in Irvine and one of our own design were used. In these computational tests, the proposed method outperformed other recent methods as gauged by various metrics and classifiers. The analyses were accompanied by statistical tests, the results of which showed that the superiority of our method is significant and therefore strengthened these conclusions. In short, the contribution of this work is the development of a method that, on the one hand, is based on different strategies than those used in recent methods, and on the other hand, improves the performance of these methods. [ABSTRACT FROM AUTHOR]

Many investigations on the response phase of cables mainly focus on the phase shift value in the linear solution, while the effect of the higher-order approximate terms (HOAT) is often omitted. To ascertain the effect of the HOAT on response phases, instantaneous phase-frequency characteristics of a classical externally- and parametrically-excited suspended cable are investigated. The Galerkin method is used to discretize the motion equations into ordinary differential equations, and the Multiple Scales Method (MSM) is used to solve these equations. Afterward, cable responses under these two types of excitation with different Irvine parameters λ 2 and excitation frequency Ω are numerically solved, and instantaneous phase differences between the responses and excitations are obtained by using the Hilbert transform. Then, variation characteristics of the instantaneous phase differences and corresponding amplitudes are analyzed in the ( λ 2 , Ω ) plane. It is shown that if the HOAT are not considered, the phase shifts of cable response would be constants. On the contrary, if they are included, the drift term (DT) and the doubling-frequency term (DFT) in the HOAT would vary periodically with time. Due to the difference in the frequency-response equation's right-hand terms between these two excitations, response amplitudes are different, affecting the phase-frequency characteristics through the DT and the DFT. The response-excitation instantaneous phase difference amplitude p max under external and parametric excitation are both suddenly increase in the local region centered on λ 2 ≈ 3.0 and Ω ≈ 1.125 and present a near-antisymmetrical distribution. However, the sudden-change-region of the former is a long and narrow band along the axis of λ 2 in the ( λ 2 , Ω ) plane, while that of the latter is a point field. Besides, values of the former are significantly larger than of the latter. [ABSTRACT FROM AUTHOR]