Artificial Intelligent Systems and Machine Learning

Purvi Rekh ¹, Amit Thakkar ², Amit Ganatra ³

Affiliations
1 Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, Petlad, IN
2 Department of Information and Technology, Chandubhai S Patel Institute of Technology, Changa, Petlad, IN
3 U & PU Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, Petlad, IN

Abstract

With the development of Internet and the emergence of a large number of text resources, the automatic text classification has become a research hotspot. As number of training documents increases, accuracy of Text Classification increases. Traditional classifiers (Supervised learning) use only labeled data for training. Labeled instances are often difficult, expensive, or time consuming to obtain. Meanwhile unlabeled data may be relatively easy to collect. Semi-Supervised Learning makes use of both labeled and unlabeled data. Several researchers have given algorithms for Text Classification using Semi-Supervised Learning. But still improving accuracy of Text Classification using Semi-Supervised Learning is a challenge. In the iterative process in the standard Expectation Maximization (EM) based semi-supervised learning, some unlabeled samples are misclassified by the current classifier because the initial labeled samples are not enough. To overcome this limitation, an EM based Semi-Supervised Learning algorithm using Naïve Bayesian and Support vector machine is proposed in this paper to improve accuracy of text classification using semi-supervised learning.

Keywords

Expectation Maximization (EM), Naïve Bayes (NB), Support Vector Machine (SVM), Semi-Supervised Machine (SSL).

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Amit Ganatra ¹, Y. P. Kosta ¹

Affiliations
1 Patel Department of Computer Engineering, Charotar University of Science & Technology, CHARUSAT, Gujarat, IN

Abstract

Stock prediction is, so far, one of the popular topics not only for research purposes but also for commercial applications. Owing to its importance, a well-established school of concepts and techniques, including fundamental and technical analysis, has developed in recent decades. However, because these techniques or tools are based on totally different analytical approaches, they often yield contradictory results. More importantly, these analytical tools are heavily dependent on human expertise and justification in areas such as the location of reversal (or continuation) patterns, market patterns, and trend prediction. Predicting stock data with traditional time series analysis has proven to be difficult. An artificial neural network may be more suitable for the task primarily because no assumption about a suitable mathematical model has to be made prior to forecasting. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. Our focus of study is to build neural network for stock market prediction. We propose to study feed forward back propagation network and their predictive accuracy. We propose to study architecture model of neural network and its different network parameters. The study attempts to understand network parameter like momentum, learning rate, number of neurons etc. We will compare architecture and result of above models. Our aim is to build best model by studying various parameters of the neural network. And also study other related model to compare accuracy of the model. In this study we have used R tool to implement the neural network. We have taken closing price, turnover, global indices, interest rate, and inflation as a neural network input. We proposed to include other indicator like news, currency rate, and crude price as input to the neural network. We compared stock prediction accuracy by setting different network parameters. Subsequently, an attempt is made to build and evaluate a neural network with different network parameters.  Technical as well as fundamental data are used as input to the network. In benchmark comparisons, the price prediction proves to be successful.

Keywords

Classification, Neural Network, Feature Selection, Prediction, Stock Market.

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