Time series models the analyses of data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time. Here five-time series datasets with different range of observation are considered to study its performance. In this paper, moving averages (MA) of series with different periods to average over are calculated; plotted series for forecasted data against original data; compared the performance of HOLT-WINTERS with the Auto Regressive Integrated Moving Average (ARIMA) model with non-zero mean; and computed the statistic test to examining the null hypothesis for the considered time series datasets. © 2017 SERSC Australia.

Dharmendra Singh Rajput

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Basha S.M

Zhenning Y

Caytiles R.D

Iyengar N.C.S.N.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Grid and Distributed Computing

In Machine Learning and statistics attribute/feature selection is used in predictive model construction. This help the Machine in interpreting the features easily by discovering good insight and improves efficiency in predictive modeling. The objective of our research is to improve the classification accuracy by knowing the most important feature from any given dataset. In this research, we used two techniques namely Data partition and K Fold, in evaluating the importance of each feature from the randomly generated dataset with 5399 instances and 20 attributes. In Data partitioning, the attribute with lowest accuracy is filtered out. Where as in K Fold cross validation, attributes with biggest error is removed from the original dataset. In our experiments, the evaluation parameters considered are Recall. Precision and F-Measure. Finally the accuracy rate of both the techniques are compared. The finding in our research stats that K Fold approach achieves better accuracy of 97.03% than Data partitioning(96.11%) in estimating the importance of features in classification. © 2018 IEEE.

2018 8th International Conference on Communication Systems and Network Technologies (CSNT)

Evaluating the Importance of each Feature in Classification task

Objective: To investigate the impact of sarcasm in analyzing the sentiments from tweets. Design: 1. The Tweets related to five different domains are collected from the Twitter by creating Twitter developer account. 2. The Tweets are preprocessed in order to extract the features (Term Frequency, Entropy, Gain Ra-tio) from the Tweets. 3. Proposed an Iterative algorithm in updating the dictionary with Negative Phrases and sentiment words. 4. Assigned a polarity to each tweet using the Dictionary based approach. 5. Tweets with Zero scores are detected as Sarcasm tweets. 6. Analysis on Variance (AOV) Test is performed on the scores obtained. 7. Perform prediction on the scores using Machine Learning Algorithms. 8. Estimated the Mean Square Prediction Error (MSPE) using Cross Validation Outcome: The impact of sarcasm on sentiment analysis is measured in terms of Precision, Recall and F-score. Results: 1. Scores of Tweets based on sentiment words and Negative Phrases 2. Summary of Analysis on variance on Scores obtained. 3. Performance of Machine Learning Algorithms in Detecting the sarcasm from tweets. Conclusion: This paper has been presented to put forth the hypothesis that changes sentiment polarity (positive to negative) of sentences can be used as a feature for detecting sarcasm within product review length bodies of text. With the level of accuracy achieved, this scoring technique can be implemented as software for social networks. Also, these efforts may be a useful tool for learning about patterns in sarcasm or making better dictionaries of offensive words. © 2018 Bentham Science Publishers.

Recent Patents on Computer Science

Parsing Based Sarcasm Detection from Literal Language in Tweets

The classification task is to predict the value of the target variable from the values of the input variables. If a target is provided as part of the dataset, then classification is a supervised task. It is important to analysis the performance of supervised classification models before using them in classification task. In our research we would like to propose a novel way to evaluated the performance of supervised classification models like Decision Tree and Naïve Bayes using KNIME Analytics platform. Experiments are conducted on Multi variant dataset consisting 58000 instances, 9 columns associated specially for classification, collected from UCI Machine learning repositories (http://archive.ics.uci.edu/ml/datasets/statlog+(shuttle)) and compared the performance of both the models in terms of Classification Accuracy (CA) and Error Rate. Finally, validated both the models using Metric precision, recall and F-measure. In our finding, we found that Decision tree acquires CA (99.465%) where as Naïve Bayes attain CA (90.358%). The F-measure of Decision tree is 0.984, whereas Naïve Bayes acquire 0.7045.

Fulltext

International Journal of Engineering & Technology

Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME

2018 International Conference on Advances in Computing and Communication Engineering (ICACCE)

Evaluating the Performance of Deep Learning Techniques on Classification Using Tensor Flow Application

Now a days the importance of analyzing the hidden sentiments from user reviews playing a prominent role towards increasing profitability in any organization. To address the challenges being faced in analyzing the text information and transforming the same in to polarities values with an objective of saving time in understanding the public opinion on particular product or service. Traditionally, there are different approaches carried out in transforming text data in to values based on different features of Text. In our research we make use of Stanford CoreNLP, Alias-i's Lingpipe (uses Logistic regression for document classification), Senti WordNet and synthesize libraries from different sources to include several other techniques that are used for text mining to evaluate the impact of feature selection on overall sentiment analysis by scoring a sentences in a review using different scoring Techniques. we also included NTU Lib Linear to make use of linear SVM for document classification. The Features considered on our experiments are Term Frequency and N-Gram (1Gram &amp; 2Gram) with Decision Tree as Prediction model to evaluate the Accuracy, Area under ROC Curve and Kappa value. Finally, Compared the polarities of the reviews obtained using three different sentiment scoring approaches. The findings in our research is, Term Frequency have good impact of (0.932) on classifying the sentiment, In contrast, 2Gram have an impact of (0.8505). © 2017 Association for Computing Machinery.

Journal	International Journal of Grid and Distributed Computing
Publisher	NADIA
ISSN	2005-4262
Open Access	0