The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: exploration of some issues

SubbaNarasimha, P.N.; Arinze, Bay; Anandarajan, Murugan

doi:10.1016/s0957-4174(00)00026-9

Cited by 34 publications

(12 citation statements)

References 14 publications

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“…The skewness of the data set might also be beneficial for analysis. SubbaNarasimha et al [85] state that data sets with skewed variables are well suited for testing the performance of various solution algorithms. The skewness of the data set is an important factor in the validity of the performance analysis of alternative solution methodologies.…”

Section: Resultsmentioning

confidence: 99%

Markovian analysis for automatic new topic identification in search engine transaction logs

Özmutlu¹

2009

Appl Stoch Models Bus & Ind

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Topic analysis of search engine user queries is an important task, since successful exploitation of the topic of queries can result in the design of new information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in analysis of search engine user queries. This study presents an application of Markov chains in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals, query reformulation patterns and the continuation/shift status of the previous query. The findings show that Markov chains provide fairly successful results for automatic new topic identification with a high level of estimation for topic continuations and shifts.

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Section: Resultsmentioning

confidence: 99%

Markovian analysis for automatic new topic identification in search engine transaction logs

Özmutlu¹

2009

Appl Stoch Models Bus & Ind

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“…The major trend illustrated in the literature in recent decades is to move away from using statistical methods as the amount of data being analysed increases [26][27][28]. ML algorithms can be classified into two general categories based on the way they "learn" about data; supervised learning and unsupervised learning.…”

Section: Approachesmentioning

confidence: 99%

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue

Wang

Chen

et al. 2018

Designs

213

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

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“…A recent article by SubbaNarisimha, Arinze, and Anandarajan (2000) categorizes NN studies by data type (real world or simulated) and dependent variable measurement (nominal/categorical or interval/ratio). The authors cite only three studies that compare NNs to multivariate statistical methods using real-world data, rather than simulated data, measured in interval-or ratio-scale: predicting student grade point averages (GPAs; Gorr, Nagin, & Szczypula, 1994), forecasting prepayment rates for mortgage-backed securities as data quality varies (Bansal, Kauffman, & Weitz, 1993), and performance prediction for transportation models (Duliba, 1991).…”

mentioning

confidence: 99%

Neural Network Modeling in Cross-Cultural Research: A Comparison with Multiple Regression

Palocsay¹,

White

2004

Organizational Research Methods

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This article describes the use of neural networks as an alternative method to investigate the links between various dimensions of culture and perceptions of justice and demonstrates their ability to model the data relationships with higher accuracy than multiple regression analysis. A complete discussion of the development and validation of the neural network models is included as a guide to researchers in management who are interested in exploring this methodology.

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The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: exploration of some issues

Cited by 34 publications

References 14 publications

Markovian analysis for automatic new topic identification in search engine transaction logs

Markovian analysis for automatic new topic identification in search engine transaction logs

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Neural Network Modeling in Cross-Cultural Research: A Comparison with Multiple Regression

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