Search (2 results, page 1 of 1)

  • × author_ss:"Wang, H."
  • × year_i:[2010 TO 2020}
  1. Wang, H.; Hong, M.: Supervised Hebb rule based feature selection for text classification (2019) 0.00 
    0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 5036) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=5036,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 5036, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5036)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Text documents usually contain high dimensional non-discriminative (irrelevant and noisy) terms which lead to steep computational costs and poor learning performance of text classification. One of the effective solutions for this problem is feature selection which aims to identify discriminative terms from text data. This paper proposes a method termed "Hebb rule based feature selection (HRFS)". HRFS is based on supervised Hebb rule and assumes that terms and classes are neurons and select terms under the assumption that a term is discriminative if it keeps "exciting" the corresponding classes. This assumption can be explained as "a term is highly correlated with a class if it is able to keep "exciting" the class according to the original Hebb postulate. Six benchmarking datasets are used to compare HRFS with other seven feature selection methods. Experimental results indicate that HRFS is effective to achieve better performance than the compared methods. HRFS can identify discriminative terms in the view of synapse between neurons. Moreover, HRFS is also efficient because it can be described in the view of matrix operation to decrease complexity of feature selection.
    Type
    a
  2. Haraty, M.; Wang, Z.; Wang, H.; Iqbal, S.; Teevan, J.: Design and in-situ evaluation of a mixed-initiative approach to information organization (2017) 0.00 
    0.0014647468 = product of:
  0.0029294936 = sum of:
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = weight(_text_:a in 3793) [ClassicSimilarity], result of:
          0.005858987 = score(doc=3793,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11032722 = fieldWeight in 3793, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3793)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Organizing personal information by folders or tags has proved to be effective for finding, remembering, and understanding information. However, past studies have shown that the cost of organization can be too high for some users to be worth the effort. Mixed-initiative approaches attempt to reduce the burden of manual organization by automatically identifying and suggesting organizational units such as folders to users. However, little is known about how such mixed-initiative approaches influence users' organizational experiences. In this paper, we explore a mixed-initiative approach that suggests high-level organizational units to users to facilitate e-mail organization. In 2 in-situ experiments with 34 knowledge workers, we study how our mixed-initiative approach influenced users' experience with organization. We show that our approach made it easier to create organizational units without negatively affecting recall of those units, and led to the creation of units that otherwise would have not been created. Our findings suggest ways computers and people can most effectively work together to organize information.
    Type
    a

Authors