Quantum K-nearest neighbor classification algorithm based on Hamming distance

K-nearest neighbor classification algorithm is one of the most basic algorithms in machine learning, which determines the sample's category by the similarity between samples. In this paper, we propose a quantum K-nearest neighbor classification algorithm with Hamming distance. In this algorithm, quantum computation is firstly utilized to obtain Hamming distance in parallel. Then, a core sub-algorithm for searching the minimum of unordered integer sequence is presented to find out the minimum distance. Based on these two sub-algorithms, the whole quantum frame of K-nearest neighbor classification algorithm is presented. At last, it is shown that the proposed algorithm can achieve a quadratical speedup by analyzing its time complexity briefly.Comment: 8 pages,5 figure

Klasifikasi Text Berita dengan Weight Adjusted K-Nearest Neighbor

ABSTRAKSI: Klasifikasi teks adalah salah satu permasalahan dalam text mining. Banyak metode yang dapat digunakan untuk menyelesaikan masalah tersebut. Salah satu metode klasifikasi adalah Weight Adjusted K-Nearest Neighbor(WAKNN). Metode ini adalah metode yang didasarkan pada K-Nearest Neighbor yang merupakan salah satu algoritma learning yang sangat efektif untuk berbagai domain permasalahan. Namun, K-Nearest Neighbor dianggap kurang efektif dalam pengukuran similarity/kemiripan sifat antara satu dokumen dengan dokumen yang lainnya karena memakai semua term yang terdapat dalam dokumen tersebut baik penting maupun tidak. Sedangkan pada Weigth Adjusted K-Nearest Neighbor akan menghitung serta mengevaluasi bobot kata dari setiap dokumen untuk menentukan kata-kata penting dari suatu kelas sehingga pada proses klasifikasi, setiap dokumen akan dibandingkan antara satu dengan yang lainnya sesuai dengan kata-kata penting yang dimilikinya. Pada tugas akhir ini, akan dicoba untuk mengklasifikasikan teks berita berbahasa Indonesia dengan menggunakan Weigth Adjusted K-Nearest Neighbor. Parameter yang akan diuji adalah precision, recall, dan f-measure. Berdasarkan hasil pengujian, WAKNN terbukti menghasilkan tingkat akurasi yang lebih baik daripada KNN Kata Kunci : Klasifikasi teks berita, Weigth Adjusted K-Nearest Neighbor, K-Nearest NeighborABSTRACT: Text classification is one of problems in text mining. Many methods that can be used to solve this problem. One of those methods is Weight Adjusted K-Nearest Neighbor(WAKNN). This method is based on the K-Nearest Neighbor classification paradigm which is proved very effective for many problems. But, K-Nearest Neighbor seems to be less effective in similarity measurement because it uses all terms in a document without consider the importances of those terms. Whereas in Weight Adjusted K-Nearest Neighbor, it will count and evaluate the weight from each term in a document for choosing some important terms from each class so that in the classification process, one document will be compared to another document by using their important terms. In this final project, it will try to classify news text in Bahasa Indonesia by using Weight Adjusted K-Nearest Neighbor. Some parameters that will be tested are precision, recall, and f-measure. Refers to the result of the experiment, WAKNN is proved giving better accuracy than KNN.Keyword: Text classification, Weigth Adjusted k-Nearest Neighbor, K-Nearest Neighbo

DTW-Global Constraint Learning Using Tabu Search Algorithm

AbstractMany methods have been proposed to measure the similarity between time series data sets, each with advantages and weaknesses. It is to choose the most appropriate similarity measure depending on the intended application domain and data considered. The performance of machine learning algorithms depends on the metric used to compare two objects. For time series, Dynamic Time Warping (DTW) is the most appropriate distance measure used. Many variants of DTW intended to accelerate the calculation of this distance are proposed. The distance learning is a subject already well studied. Indeed Data Mining tools, such as the algorithm of k-Means clustering, and K-Nearest Neighbor classification, require the use of a similarity/distance measure. This measure must be adapted to the application domain. For this reason, it is important to have and develop effective methods of computation and algorithms that can be applied to a large data set integrating the constraints of the specific field of study. In this paper a new hybrid approach to learn a global constraint of DTW distance is proposed. This approach is based on Large Margin Nearest Neighbors classification and Tabu Search algorithm. Experiments show the effectiveness of this approach to improve time series classification results