A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

Feature selection is a widespread preprocessing step in the data mining field. One of its purposes is to reduce the number of original dataset features to improve a predictive model’s performance. Despite the benefits of feature selection for the classification task, to the best of our knowledge, few studies in the literature address feature selection for the hierarchical classification context. This paper proposes a novel feature selection method based on the general variable neighborhood search metaheuristic, combining a filter and a wrapper step, wherein a global model hierarchical classifier evaluates feature subsets. We used twelve datasets from the proteins and images domains to perform computational experiments to validate the effect of the proposed algorithm on classification performance when using two global hierarchical classifiers proposed in the literature. Statistical tests showed that using our method for feature selection led to predictive performances that were consistently better than or equivalent to that obtained by using all features with the benefit of reducing the number of features needed, which justifies its efficiency for the hierarchical classification scenario

Moscas-das-frutas (Diptera: Tephritidae) em um pomar de goiabeira, no semiárido brasileiro

As moscas-das-frutas (Diptera: Tephritidae) são pragas-chave na cultura da goiabeira Psidium guajava L., com predominância de diferentes espécies de acordo com a região produtora no Brasil. Os objetivos do presente trabalho foram conhecer a diversidade e analisar parâmetros faunísticos das moscas-das-frutas obtidas em um pomar de goiabeira, no município de Cruzeta, Rio Grande do Norte, situado no semiárido brasileiro. As moscas-das-frutas foram coletadas semanalmente, com auxílio de armadilhas McPhail, tendo como atrativo proteína hidrolisada a 5% v/v. Foram registradas cinco espécies no pomar estudado: Ceratitis capitata (Wied.), Anastrepha zenildae Zucchi, Anastrepha sororcula Zucchi, Anastrepha obliqua (Macquart) e Anastrepha dissimilis Stone. Ceratitis capitata foi a espécie mais frequente, constante e dominante, considerada como uma praga invasiva, potencial em pomares de goiabeira no semiárido brasileiro

Impact of neuraminidase inhibitors on influenza A(H1N1)pdm09‐related pneumonia: an individual participant data meta‐analysis

BACKGROUND: The impact of neuraminidase inhibitors (NAIs) on influenza‐related pneumonia (IRP) is not established. Our objective was to investigate the association between NAI treatment and IRP incidence and outcomes in patients hospitalised with A(H1N1)pdm09 virus infection. METHODS: A worldwide meta‐analysis of individual participant data from 20 634 hospitalised patients with laboratory‐confirmed A(H1N1)pdm09 (n = 20 021) or clinically diagnosed (n = 613) ‘pandemic influenza’. The primary outcome was radiologically confirmed IRP. Odds ratios (OR) were estimated using generalised linear mixed modelling, adjusting for NAI treatment propensity, antibiotics and corticosteroids. RESULTS: Of 20 634 included participants, 5978 (29·0%) had IRP; conversely, 3349 (16·2%) had confirmed the absence of radiographic pneumonia (the comparator). Early NAI treatment (within 2 days of symptom onset) versus no NAI was not significantly associated with IRP [adj. OR 0·83 (95% CI 0·64–1·06; P = 0·136)]. Among the 5978 patients with IRP, early NAI treatment versus none did not impact on mortality [adj. OR = 0·72 (0·44–1·17; P = 0·180)] or likelihood of requiring ventilatory support [adj. OR = 1·17 (0·71–1·92; P = 0·537)], but early treatment versus later significantly reduced mortality [adj. OR = 0·70 (0·55–0·88; P = 0·003)] and likelihood of requiring ventilatory support [adj. OR = 0·68 (0·54–0·85; P = 0·001)]. CONCLUSIONS: Early NAI treatment of patients hospitalised with A(H1N1)pdm09 virus infection versus no treatment did not reduce the likelihood of IRP. However, in patients who developed IRP, early NAI treatment versus later reduced the likelihood of mortality and needing ventilatory support

An Efficient Tabu Search Heuristic for the School Timetabling Problem

The School Timetabling Problem (STP) regards the weekly scheduling of encounters between teachers and classes. Since this scheduling must satisfy organizational, pedagogical and personal costs, this problem is recognized as a very difficult combinatorial optimization problem. This work presents a new Tabu Search (TS) heuristic for STP. Two different memory based diversification strategies are presented. Computational experiments with real world instances, comparing with a previously proposed TS found in the literature, show that the proposed method produces better solutions for all instances, as well faster times are observed in the production of good quality solutions

A Tabu Search Heuristic with Efficient Diversification Strategies for the Class/Teacher Timetabling Problem

The Class/Teacher Timetabling Problem (CTTP) deals with the weekly scheduling of encounters between teachers and classes of an educational institution. Since CTTP is a NP-hard problem for nearly all of its variants, the use of heuristic methods for its resolution is justified. This paper presents an efficient Tabu Search (TS) heuristic with two different memory based diversification strategies for CTTP. Results obtained through an application of the method to a set of real world problems show that it produces better solutions than a previously proposed TS found in the literature and faster times are observed in the production of good quality solutions

Short-term planning of a work shift for open-pit mines: A case study

AbstractThis work deals with the short-term planning problem of a work shift for open-pit mines. The problem involves ore and waste fronts, shovels, heterogeneous truck fleets, and discharge points. The allocation of trucks is dynamic to allow multiple routes to be assigned to each truck. The problem consists of deciding which fronts must be mined and establishing the number of trucks, their routes, and the amount of material transported by them to each discharge point, satisfying a stripping ratio at the desired level. The objectives are to minimize the deviations from the targets for production, chemical grade, and particle size range of each control parameter at each plant and reduce the number of trucks needed for the process. To solve the problem, we developed a mixed-integer linear goal programming model and tested it using real data from an iron ore mine. The results showed that the proposed approach supports decision-makers in the sizing and allocation of truck fleets and in meeting the production and control parameter targets required by the ore processing plants according to the daily scenario, such as low availability of shovels and trucks, flexibility in ore quality, and need for increased production

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods