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FeatureGeneration inDataScience
+models+strongly+depends+on+the+input+data%252C+as+such+Feature+Engineering+(FE)+is+often+required+in+ML.+In+addition%252C+with+the+proliferation+of+ML-powered+systems%252C+especially+in+critical+contexts%252C+the+need+for+interpretability+and+explainability+becomes+increasingly+important.+Since+manual+FE+is+time-consuming+and+requires+case+specific+knowledge%252C+we+propose+KRAFT%252C+an+AutoFE+framework+that+leverages+a+knowledge+graph+to+guide+the+generation+of+interpretable+features.+Our+hybrid+AI+approach+combines+a+neural+generator+to+transform+raw+features+through+a+series+of+transformations+and+a+knowledge-based+reasoner+to+evaluate+features+interpretability+using+Description+Logics+(DL).+The+generator+is+trained+through+Deep+Reinforcement+Learning+(DRL)+to+maximize+the+prediction+accuracy+and+the+interpretability+of+the+generated+features.+Extensive+experiments+on+real+datasets+demonstrate+that+KRAFT+significantly+improves+accuracy+while+ensuring+a+high+level+of+interpretability.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.d4b6d581f0c709e610a43842532aa688%3frik%3dsalfuD3m%252b2jBzQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Feature+Generation+in+Data+Science&FORM=IRPRST&ck=E597E5F2564E0CEB31EB50E5B400FE3B&selectedIndex=82&itb=0)
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