Loading...
Please wait, while we are loading the content...
Similar Documents
Topic Classification: Hybrid Feature selection model using BPSO-MLP
| Content Provider | Semantic Scholar |
|---|---|
| Author | Satheesh, K. R. K. |
| Copyright Year | 2018 |
| Abstract | In this 21st century era of information and technology where our day to day life is filled with online activities which generate data by leaps and bounds. Majority of data being generated is of natural language/ text data format which becomes difficult to store. if this data is managed and analyzed properly has immense potential to make businesses more convenient. So, one must provide a way through to navigate and process this text data of reports for categorising the topics of interest. Since it would be a challenging task to handle high dimensional data and filter a relevant information, feature selection algorithms were used to enhance the efficiency of the topic classification and improve in a choice of selecting significant information. This study investigates the efficiency of the hybrid feature selection technique based on binary particle swarm optimization and evaluated with multi-layered perceptron to determine the quality of features. Experimental results show that the combination of multi-layered perceptron with B-PSO resulted in a good accuracy rate of 83% with a reduced number of features. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://trap.ncirl.ie/3447/1/karthikranjankunchumsatheesh.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
