66 research outputs found
Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems
Combining classifiers by majority voting (MV) has
recently emerged as an effective way of improving
performance of individual classifiers. However, the
usefulness of applying MV is not always observed and
is subject to distribution of classification outputs in a
multiple classifier system (MCS). Evaluation of MV
errors (MVE) for all combinations of classifiers in MCS
is a complex process of exponential complexity.
Reduction of this complexity can be achieved provided
the explicit relationship between MVE and any other
less complex function operating on classifier outputs is
found. Diversity measures operating on binary
classification outputs (correct/incorrect) are studied in
this paper as potential candidates for such functions.
Their correlation with MVE, interpreted as the quality
of a measure, is thoroughly investigated using artificial
and real-world datasets. Moreover, we propose new
diversity measure efficiently exploiting information
coming from the whole MCS, rather than its part, for
which it is applied
Integrating Specialized Classifiers Based on Continuous Time Markov Chain
Specialized classifiers, namely those dedicated to a subset of classes, are
often adopted in real-world recognition systems. However, integrating such
classifiers is nontrivial. Existing methods, e.g. weighted average, usually
implicitly assume that all constituents of an ensemble cover the same set of
classes. Such methods can produce misleading predictions when used to combine
specialized classifiers. This work explores a novel approach. Instead of
combining predictions from individual classifiers directly, it first decomposes
the predictions into sets of pairwise preferences, treating them as transition
channels between classes, and thereon constructs a continuous-time Markov
chain, and use the equilibrium distribution of this chain as the final
prediction. This way allows us to form a coherent picture over all specialized
predictions. On large public datasets, the proposed method obtains considerable
improvement compared to mainstream ensemble methods, especially when the
classifier coverage is highly unbalanced.Comment: Published at IJCAI-17, typo fixe
SISTEM MONITORING KUALITAS AIR AKUARIUM MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION
Hobi akuarium merupakan hobi populer yang dapat didukung dengan penggunaan teknologi. Penggunaan teknologi kecerdasan buatan dan internet of things dapat mempermudah aktivitas sehari-hari untuk memantau atau memonitoring alat atau lingkungan, salah satu lingkungan yang dapat dipantau dengan teknologi internet of things adalah kualitas air akuarium. Kualitas air akuarium dapat dipantau melalui parameter pH, temperatur, TDS, dan turbidity.
Terdapat klasifikasi manual seperti Indeks Pencemaran (IP), Quality Index (WQI) dan STORET dengan kendala waktu dan biaya yang cukup tinggi. Klasifikasi secara manual atau inferensi akan menyebabkan ketidak efisienan ketika data yang ditambahkan menggunakan parameter yang beragam. Klasifikasi manual dapat digantikan dengan metode klasifikasi otomatis atau menggunakan neural network seperti Learning Vector Quantization (LVQ).
Berdasarkan hasil penelitian hardware sudah berhasil mengakuisisi data kemudian disimpan ke database dan ditampilkan di website berikut hasil klasifikasinya. Sistem monitoring melalui pengujian hardware sudah berhasil mengirimkan data antar perangkat dan komunikasi ke website melalui API kemudian uji sensor dilakukan dengan melihat rata-rata error pada pembacaan yang kurang dari 5%. Melalui pengujian blackbox, responsivitas, dan uji penggunaan aplikasi ini sudah memenuhi standar ekspektasi penelitian berikut uji notifikasi dengan hasil pengiriman notifikasi dari website yang dapat digunakan secara fungsi. Penerapan Learning Vector Quantization mampu menghasilkan klasifikasi dengan akurasi sebesar 94%
Linear and Order Statistics Combiners for Pattern Classification
Several researchers have experimentally shown that substantial improvements
can be obtained in difficult pattern recognition problems by combining or
integrating the outputs of multiple classifiers. This chapter provides an
analytical framework to quantify the improvements in classification results due
to combining. The results apply to both linear combiners and order statistics
combiners. We first show that to a first order approximation, the error rate
obtained over and above the Bayes error rate, is directly proportional to the
variance of the actual decision boundaries around the Bayes optimum boundary.
Combining classifiers in output space reduces this variance, and hence reduces
the "added" error. If N unbiased classifiers are combined by simple averaging,
the added error rate can be reduced by a factor of N if the individual errors
in approximating the decision boundaries are uncorrelated. Expressions are then
derived for linear combiners which are biased or correlated, and the effect of
output correlations on ensemble performance is quantified. For order statistics
based non-linear combiners, we derive expressions that indicate how much the
median, the maximum and in general the ith order statistic can improve
classifier performance. The analysis presented here facilitates the
understanding of the relationships among error rates, classifier boundary
distributions, and combining in output space. Experimental results on several
public domain data sets are provided to illustrate the benefits of combining
and to support the analytical results.Comment: 31 page
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