Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

Because COVID-19 occurred in 2019, the behavioxr of humans has been changed and it will influence the business model of enterprise. Enterprise cannot predict its development according to past knowledge and experiment; so, it needs a new machine learning framework to predict enterprise performance. The goal of this research is to modify AdaBoost to reasonably predict the enterprise performance. In order to justify the usefulness of the proposed model, enterprise data will be collected and the proposed model can be used to predict the enterprise performance after COVID-19. The test data correct rate of the proposed model will be compared with some of the traditional machine learning models. Compared with the traditional AdaBoost, back propagation neural network (BPNN), regression classifier, support vector machine (SVM) and support vector regression (SVR), the proposed method possesses the better classification ability (average correct rate of the proposed method is 88.04%) in handling two classification problems. Compared with traditional AdaBoost, one-against-all SVM, one-against-one SVM, one-against-all SVR and one-against-one SVR, the classification ability of the proposed method is also relatively better for coping with the multi-class classification problem. Finally, some conclusions and future research will be discussed at the end.

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Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

Energies ◽

10.3390/en12020218 ◽

2019 ◽

Vol 12 (2) ◽

pp. 218 ◽

Cited By ~ 5

Author(s):

Nan Wei ◽

Changjun Li ◽

Jiehao Duan ◽

Jinyuan Liu ◽

Fanhua Zeng

Keyword(s):

Deep Learning ◽

Natural Gas ◽

Load Forecasting ◽

Back Propagation ◽

Principal Component ◽

Back Propagation Neural Network ◽

Support Vector ◽

Weather Factors ◽

Original Dataset ◽

Proposed Model

Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.

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ENHANCED ABC-LSSVM FOR ENERGY FUEL PRICE PREDICTION

Journal of Information and Communication Technology ◽

10.32890/jict.12.2013.8138 ◽

2013 ◽

Author(s):

Zuriani Mustaffa ◽

Yuhanis Yusof ◽

Siti Sakira Kamaruddin

Keyword(s):

Time Series Data ◽

Back Propagation ◽

Search Space ◽

Back Propagation Neural Network ◽

Series Data ◽

Support Vector ◽

Fuel Price ◽

Bee Colony ◽

Crude Oil Prices ◽

Proposed Model

This paper presents an enhanced Artificial Bee Colony (eABC) based on Lévy Probability Distribution (LPD) and conventional mutation. The purposes of enhancement are to enrich the searching behavior of the bees in the search space and prevent premature convergence. Such an approach is used to improve the performance of the original ABC in optimizing the embedded hyper-parameters of Least Squares Support Vector Machines (LSSVM). Later on, a procedure is put forward to serve as a prediction tool to solve prediction task. To evaluate the efficiency of the proposed model, crude oil prices data was employed as empirical data and a comparison against four approaches were conducted, which include standard ABC-LSSVM, Genetic Algorithm-LSSVM (GA-LSSVM), Cross Validation-LSSVM (CV-LSSVM), and conventional Back Propagation Neural Network (BPNN). From the experiment that was conducted, the proposed eABC-LSSVM shows encouraging results in optimizing parameters of interest by producing higher prediction accuracy for employed time series data.

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NONPARALLEL HYPERPLANES PROXIMAL CLASSIFIERS BASED ON MANIFOLD REGULARIZATION FOR LABELED AND UNLABELED EXAMPLES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001413500158 ◽

2013 ◽

Vol 27 (05) ◽

pp. 1350015 ◽

Cited By ~ 4

Author(s):

ZHI-XIA YANG

Keyword(s):

Linear Equations ◽

Classification Problem ◽

Twin Support Vector Machine ◽

Support Vector ◽

Manifold Regularization ◽

Classification Problems ◽

Intrinsic Structure ◽

Benchmark Datasets ◽

Multi Class Classification ◽

Proximal Classifiers

In this paper, we propose two Laplacian nonparallel hyperplane proximal classifiers (LapNPPCs) for semi-supervised and full-supervised classification problem respectively by adding manifold regularization terms. Due to the manifold regularization terms, our LapNPPCs are able to exploit the intrinsic structure of the patterns of the training set. Furthermore, our classifiers only need to solve two systems of linear equations rather than two quadratic programming (QP) problems as needed in Laplacian twin support vector machine (LapTSVM) (Z. Qi, Y. Tian and Y. Shi, Neural Netw.35 (2012) 46–53). Numerical experiments on toy and UCI benchmark datasets show that the accuracy of our LapNPPCs is comparable with other classifiers, such as the standard SVM, TWSVM and LapTSVM, etc. It is also the case that based on our LapNPPCs, some other TWSVM type classifiers with manifold regularization can be constructed by choosing different norms and loss functions to deal with semi-supervised binary and multi-class classification problems.

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Big Data Analysis of Facebook Users Personality Reconition using Map Reduce Back Propagation Neural Networks

FUOYE Journal of Engineering and Technology ◽

10.46792/fuoyejet.v6i2.594 ◽

2021 ◽

Vol 6 (2) ◽

Author(s):

Solomon Akinboro ◽

Isaac K. Ogundoyin ◽

Ayobami T. Olusesi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Back Propagation ◽

Threshold Value ◽

Back Propagation Neural Network ◽

Classification Model ◽

Map Reduce ◽

Support Vector ◽

Computationally Efficient ◽

Personality Recognition

Machine learning has been an effective tool to connect networks of enormous information for predicting personality. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance. Keywords— Machine learning, Facebook, MRBPNN, Personality Recognition, Neuroticism, Agreeableness.

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Multiclass Boosting with Adaptive Group-BasedkNN and Its Application in Text Categorization

Mathematical Problems in Engineering ◽

10.1155/2012/793490 ◽

2012 ◽

Vol 2012 ◽

pp. 1-24 ◽

Cited By ~ 6

Author(s):

Lei La ◽

Qiao Guo ◽

Dequan Yang ◽

Qimin Cao

Keyword(s):

Chinese Text ◽

Text Categorization ◽

Nearest Neighbor ◽

Classification Problem ◽

Support Vector ◽

Classification Problems ◽

Adaboost Algorithm ◽

Novel Method ◽

Categorization System ◽

Multi Class Classification

AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM), neural networks (NN), naïve Bayes, andk-nearest neighbor (kNN). This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-basedkNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-basedkNN boosting algorithm (AGkNN-AdaBoost). We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.

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Development of Machine Learning Model Using Least Square-Support Vector Machine, Differential Evolution and Back Propagation Neural Network to Detect Breast Cancer

Smart Computing Techniques and Applications - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-16-1502-3_7 ◽

2021 ◽

pp. 51-66

Author(s):

Madhura D. Vankar ◽

G. A. Patil

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Least Square ◽

Support Vector ◽

Machine Learning Model ◽

Detect Breast Cancer

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Developing Hybrid Machine Learning Models for Estimating the Unconfined Compressive Strength of Jet Grouting Composite: A Comparative Study

Applied Sciences ◽

10.3390/app10051612 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1612 ◽

Cited By ~ 7

Author(s):

Yuantian Sun ◽

Guichen Li ◽

Junfei Zhang

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Unconfined Compressive Strength ◽

Search Algorithm ◽

Back Propagation ◽

High Strength ◽

Back Propagation Neural Network ◽

Support Vector ◽

K Nearest Neighbors ◽

Jet Grouting

Coal-grout composites were fabricated in this study using the jet grouting (JG) technique to enhance coal mass in underground conditions. To evaluate the mechanical properties of the created coal-grout composite, its unconfined compressive strength (UCS) needed to be tested. A mathematical model is required to elucidate the unknown nonlinear relationship between the UCS and the influencing variables. In this study, six computational intelligence techniques using machine learning (ML) algorithms were used to develop the mathematical models, which includes back-propagation neural network (BPNN), random forest (RF), decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR). In addition, the hyper-parameters in these typical algorithms (e.g., the hidden layers in BPNN, the gamma in SVM, and the number of neighbor samples in KNN) were tuned by the recently developed beetle antennae search algorithm (BAS). To prepare the dataset for these ML models, three types of cementitious grout and three types of chemical grout were mixed with coal powders extracted from the Guobei coalmine, Anhui Province, China to create coal-grout composites. In total, 405 coal-grout specimens in total were extracted and tested. Several variables such as grout types, coal-grout ratio, and curing time were chosen as input parameters, while UCS was the output of these models. The results show that coal-chemical grout composites had higher strength in the short-term, while the coal-cementitious grout composites could achieve stable and high strength in the long term. BPNN, DT, and SVM outperform the others in terms of predicting the UCS of the coal-grout composites. The outstanding performance of the optimum ML algorithms for strength prediction facilitates JG parameter design in practice and could be the benchmark for the wider application of ML methods in JG engineering for coal improvement.

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Ensemble Classification for Skewed Data Streams Based on Neural Network

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s021848851850037x ◽

2018 ◽

Vol 26 (05) ◽

pp. 839-853 ◽

Cited By ~ 3

Author(s):

Yong Zhang ◽

Jiaxin Yu ◽

Wenzhe Liu ◽

Kaoru Ota

Keyword(s):

Neural Network ◽

Machine Learning ◽

Data Streams ◽

Data Stream ◽

Back Propagation ◽

Class Imbalance ◽

Back Propagation Neural Network ◽

Ensemble Classification ◽

Classification Problems ◽

Overall Performance

Data stream learning in non-stationary environments and skewed class distributions has been receiving more attention in machine learning communities. This paper proposes a novel ensemble classification method (ECSDS) for classifying data streams with skewed class distributions. In the proposed ensemble method, back-propagation neural network is selected as the base classifier. In order to demonstrate the effectiveness of our proposed method, we choose three baseline methods based on ECSDS and evaluate their overall performance on ten datasets from UCI machine learning repository. Moreover, the performance of incremental learning is also evaluated by these datasets. The experimental results show our proposed method can effectively deal with classification problems on non-stationary data streams with class imbalance.

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The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7070274 ◽

2018 ◽

Vol 7 (7) ◽

pp. 274 ◽

Cited By ~ 6

Author(s):

Sornkitja Boonprong ◽

Chunxiang Cao ◽

Wei Chen ◽

Xiliang Ni ◽

Min Xu ◽

...

Keyword(s):

Machine Learning ◽

Satellite Image ◽

Back Propagation ◽

Speckle Noise ◽

Remotely Sensed ◽

Back Propagation Neural Network ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Remotely Sensed Data

Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.

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