In this paper a more improved Feature Selection and Classification technique is implanted on Benchmark Datasets such as Mushroom and Soyabean. The Proposed Methodology implemented is based on the Hybrid Combinatorial method of Applying PSO-SVM for the selection of Features from the Dataset and Then Classification is done using Fuzzy Based Decision Tree. Experimental results when performed on Various Datasets prove that the proposed methodology extracts more features as well as provides more accuracy as compared to existing methodologies.
Software testing is one of the most important parts of the software development. It also takes too much time to complete, because there are test cases are used for the testing of the software. So data mining techniques are used to improve the performance of the testing by reducing the size of the test cases. In this paper a Parallel Early Binding Recursive Ant Colony (PEB-RAC) System technique is presented with automated testing to provide an efficient way of the software testing. A result analysis is shown in the result section, this analysis shows that proposed technique provide better result as compare to the other technique.
Mining is way of providing and extracting some meaning information from the data so that the data can be classified and grouped easily and quickly. These mining algorithms can be applied in various fields including classification of agricultural crops production. In the fields of Data Mining various efficient algorithms are implemented for the classification of agricultural crops production. Here in this paper a survey of all the existing techniques as well as their advantages and issues are discussed. Hence by analyzing their various advantages and issues a new and efficient technique for the classification of agricultural crops production is proposed in future such as classification using Fuzzy Conclusion Tree by the Optimizing the Feature Withdrawal using PSO-SVM (Particle Swarm Optimization with Support Vector Machine).
Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases . Now a day's large amount of data is generated, that need to be analyse, and pattern have to be extracted from that to get some knowledge. Classification is a supervised machine learning task which builds a model from labelled training data. The model is used for determining the class; there are many types of classification algorithms such as tree-based algorithms (C4.5 decision tree, j48 decision tree etc.), naive Bayes and many more. These classification algorithms have their own pros and cons, depending on many factors such as the characteristics of the data. We can measure the classification performance by using several metrics, such as accuracy, precision, classification error and kappa on the testing data. We have used a random dataset in a rapid miner tool for the classification. Stratified sampling is used in different classifier such as J48, C4.5 and naïve Bayes. We analysed the result of the classifier using the randomly generated dataset and without random dataset.
Email spam is one of the unsolved problems of the today's Internet, annoying individual users and bringing financial damage to companies. Among the approaches developed to stop spam emails, filtering is a popular and important one. Common uses for email filters include organizing incoming email and computer viruses and removal of spam. As spammers periodically find new ways to bypass spam filters and distribute spam messages, researchers need to stay on the forefront of this problem to help reduce the amount of spam messages. Currently spam emails occupy more than 70% of all email traffic. The negative effect spam has on companies is greatly related to financial aspects and the productivity of employees in the workplace. In this paper, we propose the new approach to classify spam emails using support vector machine. It found that the SVM outperformed than other classifiers.
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