Journal ArticleDOI
Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction
M. M. Gowthul Alam,S. Baulkani +1 more
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TLDR
The optimization strategy proposed here may be used as a promising choice forecasting tool for better generalization ability higher forecasting accuracy and is proved by three different stock market datasets, which demonstrate that the proposed approach outperforms the MKSVM with default parameter, MKsVM with PSO, MKS VM with GA and other methods.Abstract:
In this paper, a novel multi-kernel support vector machine (MKSVM) combining global and local characteristics of the input data is proposed. Along with, a parameter tuning approach is developed using the fruit fly optimization (FFO), which is applied to stock market movement direction prediction problem. At first, factor analysis is used for identifying reduced key features called as factor scores from the raw stock index data which when applied to the model contributes to improvement in prediction performance. Subsequently, a hybrid kernel method combining local and global characteristics of input data is proposed, where polynomial is used for global kernel and radial basis function is utilized for local kernel. Additionally, FFO-based parameter tuning scheme is proposed to enhance the prediction performance further. Lastly, the evolving MKSVM with best feature subset and optimal parameters is used to predict stock market movement direction based upon historical data series. For evaluation and illustration purposes, three significant stock databases, NYSE, DJI and S&P 500 are used as testing targets. The effectiveness of this proposed approach is proved by three different stock market datasets, which demonstrate that the proposed approach outperforms the MKSVM with default parameter, MKSVM with PSO, MKSVM with GA and other methods. In addition, our findings reveal that the optimization strategy proposed here may be used as a promising choice forecasting tool for better generalization ability higher forecasting accuracy.read more
Citations
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Journal ArticleDOI
Machine learning techniques and data for stock market forecasting: A literature review
TL;DR: In this paper , a review of machine learning techniques applied for stock market prediction is presented, focusing on the stock markets investigated in the literature as well as the types of variables used as input in the machine learning methods used for predicting these markets.
Journal ArticleDOI
CNN-OHGS: CNN-oppositional-based Henry gas solubility optimization model for autonomous vehicle control system
S. Ravikumar,D. Kavitha +1 more
TL;DR: A convolutional neural network‐oppositional‐based Henry gas solubility optimization (CNN‐OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle and has outperformed other existing approaches.
Journal ArticleDOI
An Adaptive Neuro-Fuzzy Inference System and Black Widow Optimization Approach for Optimal Resource Utilization and Task Scheduling in a Cloud Environment
TL;DR: In this article, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-Black Widow Optimization (BWO) method is used to establish the proper VM for every task with less delay.
Journal ArticleDOI
Mango leaf disease identification and classification using a CNN architecture optimized by crossover-based levy flight distribution algorithm
TL;DR: This work proposes a novel framework for mango leaves disease classification using the pre-trained MobileNetV2 model and demonstrates superior classification performances over other state-of-art methods.
Journal ArticleDOI
HRDSS-WMSN: A Multi-objective Function for Optimal Routing Protocol in Wireless Multimedia Sensor Networks using Hybrid Red Deer Salp Swarm algorithm
S. Ambareesh,A. Neela Madheswari +1 more
TL;DR: The comparative analysis is done and the results reveal that the proposed HDRSS approach provides the best optimal routing path when compared with various approaches.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Another look at measures of forecast accuracy
Rob J. Hyndman,Anne B. Koehler +1 more
TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.
Journal ArticleDOI
Evolutionary computation: comments on the history and current state
TL;DR: The purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA), evolution strategies (ES), and evolutionary programming (EP) are described by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism).