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Ingale, Maya
- Analysis of Performance of Classifier Algorithms for Different Text Representations
Abstract Views :179 |
PDF Views:2
Our paper provides brief overview of popular text representation techniques along with the analysis of performance of three major text classifiers against the three popular text representations of vector space model, graph based model and NMF based model in the multi label setting. We are also proposing mltcNMF, feature extraction algorithm based on non negative matrix factorization approach in the high dimensional data space. We conducted set of experiments to make comprehensive evaluation of the effects of these text representation approaches using multi label datasets and also measured classification performance of our new algorithm. Our empirical study shows that use of appropriate feature selection strategy in text representation can significantly improves performance of text classification system.
Authors
Affiliations
1 Program in Computer Science, DAU, Indore, IN
2 Devi Ahilya Vishwa Vidyalaya, Indore, IN
3 EKlat-Research, Pune, IN
1 Program in Computer Science, DAU, Indore, IN
2 Devi Ahilya Vishwa Vidyalaya, Indore, IN
3 EKlat-Research, Pune, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 1 (2013), Pagination: 25-29Abstract
Text representation has a strong impact on the performance of text classification system. Text representation with high and redundant number of features, noisy and irrelevant features often increases training and classification time of text classification system. It also reduces accuracy of system. An appropriate text representation with properly extracted or selected features may lead to high accuracy.Our paper provides brief overview of popular text representation techniques along with the analysis of performance of three major text classifiers against the three popular text representations of vector space model, graph based model and NMF based model in the multi label setting. We are also proposing mltcNMF, feature extraction algorithm based on non negative matrix factorization approach in the high dimensional data space. We conducted set of experiments to make comprehensive evaluation of the effects of these text representation approaches using multi label datasets and also measured classification performance of our new algorithm. Our empirical study shows that use of appropriate feature selection strategy in text representation can significantly improves performance of text classification system.
Keywords
Text Classification, Vector Space Model, NMF, Multi Label Text Classification.
- Hand Posture Recognition for Complex Decision Making
Abstract Views :181 |
PDF Views:1
We also have described another method which uses previous frame as its context and recognize the gesture performed. Many a times, a gesture recognized can have multiple meaning, or its actual meaning cannot be easily recognized by just extracting features from one frame. So, in such situations we can use previous frame of the gesture performed and extract its features. The information obtained from these features can then be used to avoid the ambiguity in meaning and the gesture can be recognized accurately. These gestures then can be used to make some complex decisions, ultimately driving an application.
Authors
Affiliations
1 Maharashtra Institute of Technology, College of Engineering, Pune, IN
2 School of Computer Science and Information Technology, D.A. University, IN
1 Maharashtra Institute of Technology, College of Engineering, Pune, IN
2 School of Computer Science and Information Technology, D.A. University, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 4 (2012), Pagination: 186-190Abstract
Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machine and humans than primitive text, user interfaces or even GUI’s. Gesture recognition method used in this paper aims on recognizing hand postures as well as facial expressions. Here, facial expressions are used as context for the gesture recognized that helps in giving additional information about the gestures performed. Thus the information obtained from this static posture defines a particular gesture.We also have described another method which uses previous frame as its context and recognize the gesture performed. Many a times, a gesture recognized can have multiple meaning, or its actual meaning cannot be easily recognized by just extracting features from one frame. So, in such situations we can use previous frame of the gesture performed and extract its features. The information obtained from these features can then be used to avoid the ambiguity in meaning and the gesture can be recognized accurately. These gestures then can be used to make some complex decisions, ultimately driving an application.