Neural network for multi-class classification by boosting composite stumps

Nie, Qingfeng; Jin, Lei; Fei, Shumin; Ma, Junyong

doi:10.1016/j.neucom.2014.07.039

Cited by 16 publications

(9 citation statements)

References 27 publications

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“…This kind of classification is complex as each sample may belong to more than one class and predictions of one level is fed as inputs to next level to make a final decision (Cerri, Barros, & Carvalho, 2014). Also in a similar setup, linear regression could be used for feature selection in an ensemble boosted classifier (Nie, Jin, Fei, & Ma, 2015). Neural network forms the base of the ensemble with the help of composite stumps.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

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Section: Artificial Neural Networkmentioning

confidence: 99%

“…Further, over fitting is taken care of by Adaboost and accuracy is maintained through ANNs (Nie et al, 2015).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

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“…In this work, we select three classification methods i.e., NNCS [35], MWLSTSVH [36] and DLSR [37], to divide the joint feature vector of EOH and MSPLBP. These classification results are used to evaluate the performance of our system.…”

Section: Classificationmentioning

confidence: 99%

“…Considering this problem, MSPLBP expands the lateral scale to improve the description of the texture size and the texture structure, and merges the paths to reduce the feature dimensions. Finally, three state-of-the-art classifiers i.e., NNCS [35], MWLSTSVH [36], and DLSR [37] are used to classify the fused spatial edge and texture feature of the 3D lung entities into 6 malignancy levels.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

Wang

Cui

et al. 2020

IEEE Access

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Accurate malignancy level classification of lung nodules can reduce lung cancer mortality rate effectively. In this study, we present a fast and efficient CAD system to improve the performance of nodules malignancy level classification. Firstly, to reduce false positives (FPs), we propose a novel vessel segmentation method which measures vessel likelihood by tubular-like structures discriminating from multiple views. The method can recognize irregular vascular structures robustly and sensitively, and achieve fast vessel segmentation. In addition, a mathematical description for 3D pulmonary entities using neighbor centroids clustering is provided as a fundamental condition for spatial feature extraction. To optimize features extraction, we formulate a gray values cumulative function and a patches selection function based on the mathematical description, to generate axial spatial outline and spatial density distribution samples of the entities, respectively. Then, we use Edge Orientation Histogram (EOH) to extract edge features from the spatial outline and propose a multi-scale path LBP (MSPLBP) to extract the texture feature of the density distribution samples. Finally, the fused EOH and MSPLBP are classified into 6 malignancy levels by three state-of-the-art classifiers. The experimental results show that the vessel segmentation method achieves an average F1_Score of 78.14% and AUC value under PR curves of 0.8149. Moreover, our system reaches an average accuracy of 95.88% and consumes average 176.26 seconds for evaluating a CT set on malignancy level classification. These results indicate that the system can segment vessels exactly, and classify the malignancy level of nodules efficiently. Our system is the potential to be a powerful tool for early diagnosis of lung cancer.

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“…There are various transfer functions used in neuron structure in literature [18]. The neuron output, a is given in (3) a=f(wp +b)…”

Section: N=wp+b (2)mentioning

confidence: 99%

The Performance Analysis of Artificial Neural Network Based Shimizu-Morioka Chaotic System with Respect to Sample Numbers

Alçın¹,

Pehlivan²,

Koyuncu³

2015

Balkan Journal of Electrical and Computer Engineering

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In this paper, Shimizu-Morioka Chaotic System (SMCS) is modelled using Feed Forward Artificial Neural Network. In the realized network model, Log-Sigmoid and Purelin transfer functions have been used for hidden and output layer, respectively. 3-10-3 network structure is created using MATLAB. The model inputs are the state variables of SMCS. Outputs represent not only the outputs of SMCS but also iterative versions of these inputs. For the equations' numeric solutions of describing SMCS, Runge Kutta 5 Butcher (RK-5-B) algorithm which is one of the differential equation solution methods, is used. Samples in the structure of described network, the created different numbers of samples using RK-5-B have been used as input data and performance analysis have been performed for these data. As a result, the paper shows that when the sample data numbers increase, network modeling performance gives more successful results.

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Neural network for multi-class classification by boosting composite stumps

Cited by 16 publications

References 27 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

A Fast and Efficient CAD System for Improving the Performance of Malignancy Level Classification on Lung Nodules

The Performance Analysis of Artificial Neural Network Based Shimizu-Morioka Chaotic System with Respect to Sample Numbers

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