000			02125nam a22002417a 4500
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008			251127b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aQA76.9 Ar4 2017
082			_a.
100	1		_aPaola Allan Atgenal and Michael Tulagan.
245			_aAn enhancement of random forest algorithm applied in credit card fraud detection system
264		1	_a. _b. _cc2017
300			_bUndergraudate Thesis: (BSCS major in Computer Science)- Pamantasan ng Lungsod ng Maynila, 2017.
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT One of the most popular frameworks used by data scientists is the random forest algorithm. It is one of the most accurate learning algorithms available. For many data sets, it produces a highly accurate classifier. The random forest algorithm is one of the best among classification algorithms able to classify large amounts of data with accuracy. This study aims to improve the algorithms accuracy by applying our solutions to the problems that always occur in the algorithm. The results should make the algorithms accuracy more accurate in its predictive performance in finding fraudulent transactions inside an e-commerce website single decision trees often have high variance or high bias. Random forest attempts to mitigate the problem of high variance and high bias by engaging to find a natural balance between the attributes that have been used. We have used sampling technique to cut out one third of unnecessary data sets to produce a reliable prediction to our data sets. The results of learning are incomprehensible. Compared to a single decision tree, or to a set of rules, they don't give a lot of insight. Researchers should also improve the tree structure instead of just improving the accuracy itself. Instead of having a big tree structure researchers should also focus on pre-building the tree to select the right attributes on building the tree.
526			_aF
655			_aacademic writing
942			_2lcc _cARCHIVES
999			_c37232 _d37232